Computational Imaging & VisAnalysis

#360: Y. Y. Wang, A. S.Hamad, K. Palaniappan, T. E.Lever, and F. Bunyak

LARNet-STC: Spatio-temporal orthogonal region selection network for laryngeal closure detection in endoscopy videos

Computers in Biology and Medicine, pgs. 105339, 2022

deep learning, vocal folds, laryngeal endoscopy, laryngeal closure detection, laryngeal adductor reflex

Abstract, Bibtex, PlainText, URL, DOI, Google Scholar

@article{wang2022larnet,
  author = "Y. Y. Wang and A. S.Hamad and K. Palaniappan and T. E.Lever and F. Bunyak",
  title = "LARNet-STC: Spatio-temporal orthogonal region selection network for laryngeal closure detection in endoscopy videos",
  year = 2022,
  journal = "Computers in Biology and Medicine",
  publisher = "Elsevier",
  pages = "105339",
  month = "Feb",
  keywords = "deep learning, vocal folds, laryngeal endoscopy, laryngeal closure detection, laryngeal adductor reflex",
  doi = "https://doi.org/10.1016/j.compbiomed.2022.105339",
  url = "https://www.sciencedirect.com/science/article/abs/pii/S0010482522001317#!"
}

Y. Y. Wang, A. S.Hamad, K. Palaniappan, T. E.Lever, and F. Bunyak. LARNet-STC: Spatio-temporal orthogonal region selection network for laryngeal closure detection in endoscopy videos. Computers in Biology and Medicine, Elsevier, pages 105339, February 2022.

#359: J. Morris, S. Newman, K. Palaniappan, J. Fan, and D. Lin

Do you know you are tracked by photos that you didn't take: Large-scale location-aware multi-party image privacy protection

IEEE Transactions on Dependable and Secure Computing, pgs. In Press, 2022

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@article{DanLin2022:multi-party-image-privacy,
  author = "J. Morris and S. Newman and K. Palaniappan and J. Fan and D. Lin",
  title = "Do you know you are tracked by photos that you didn't take: Large-scale location-aware multi-party image privacy protection",
  year = 2022,
  journal = "IEEE Transactions on Dependable and Secure Computing",
  pages = "In Press",
  doi = "10.1109/TDSC.2021.3132230"
}

J. Morris, S. Newman, K. Palaniappan, J. Fan, and D. Lin. Do you know you are tracked by photos that you didn't take: Large-scale location-aware multi-party image privacy protection. IEEE Transactions on Dependable and Secure Computing, pages In Press, 2022.

#357: D. K. Ufuktepe, J. Collins, E. Ufuktepe, J. Fraser, T. Krock, and K. Palaniappan

Learning-based shadow Detection in Aerial Imagery Using Automatic Training Supervision from 3D Point Clouds

IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Workshop on Analysis of Aerial Motion Imagery (WAAMI), 2021

Abstract, Bibtex, PlainText, Google Scholar

@inproceedings{2021a,
  author = "D. K. Ufuktepe and J. Collins and E. Ufuktepe and J. Fraser and T. Krock and K. Palaniappan",
  title = "Learning-based shadow Detection in Aerial Imagery Using Automatic Training Supervision from 3D Point Clouds",
  year = 2021,
  journal = "IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Workshop on Analysis of Aerial Motion Imagery (WAAMI)",
  month = "Oct"
}

D. K. Ufuktepe, J. Collins, E. Ufuktepe, J. Fraser, T. Krock, and K. Palaniappan. Learning-based shadow Detection in Aerial Imagery Using Automatic Training Supervision from 3D Point Clouds. IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Workshop on Analysis of Aerial Motion Imagery (WAAMI), October 2021.

#358: K. Nouduri, K. Gao, J. Fraser, S. Yao, H. AliAkbarpour, F. Bunyak, and K. Palaniappan

Deep realistic novel view generation for city-scale aerial images

IEEE International Conference on Pattern Recognition (ICPR), pgs. 7, 2021

3d voxel rendering, denoising, convolutional neural networks, synthetic aerial imagery, point cloud evaluation

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Rohit2021:ICPR_deep-realistic-views,
  author = "K. Nouduri and K. Gao and J. Fraser and S. Yao and H. AliAkbarpour and F. Bunyak and K. Palaniappan",
  title = "Deep realistic novel view generation for city-scale aerial images",
  year = 2021,
  journal = "IEEE International Conference on Pattern Recognition (ICPR)",
  pages = "7",
  month = "Jan",
  keywords = "3d voxel rendering, denoising, convolutional neural networks, synthetic aerial imagery, point cloud evaluation",
  doi = "10.1109/ICPR48806.2021.9412844"
}

K. Nouduri, K. Gao, J. Fraser, S. Yao, H. AliAkbarpour, F. Bunyak, and K. Palaniappan. Deep realistic novel view generation for city-scale aerial images. IEEE International Conference on Pattern Recognition (ICPR), pages 7, January 2021.

#352: M. M. H. Shuvo, Y. M. Kassim, F. Bunyak, O. V. Glinskii, L. Xie, V. V. Glinsky, V. H. Huxley, M. M. Thakkar, and K. Palaniappan

Multi-focus Image Fusion for Confocal Microscopy Using U-Net Regression Map

International Conference on Pattern Recognition (ICPR), pgs. 4317-4323, 2021

Bibtex, PlainText, DOI, Google Scholar

@inproceedings{Shuvo:ICPR2021,
  author = "M. M. H. Shuvo and Y. M. Kassim and F. Bunyak and O. V. Glinskii and L. Xie and V. V. Glinsky and V. H. Huxley and M. M. Thakkar and K. Palaniappan",
  title = "Multi-focus Image Fusion for Confocal Microscopy Using U-Net Regression Map",
  year = 2021,
  booktitle = "International Conference on Pattern Recognition (ICPR)",
  pages = "4317-4323",
  doi = "10.1109/ICPR48806.2021.9412122"
}

#351: T. Hajilounezhad, R. Bao, K. Palaniappan, F. Bunyak, P. Calyam, and M. R. Maschmann

Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

npj Computational Materials, Volume 7, pgs. 1--11, 2021

Bibtex, PlainText, Google Scholar

@article{CNT:NPJ2021,
  author = "T. Hajilounezhad and R. Bao and K. Palaniappan and F. Bunyak and P. Calyam and M. R. Maschmann",
  title = "Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning",
  year = 2021,
  journal = "npj Computational Materials",
  publisher = "Nature Publishing Group",
  volume = 7,
  number = 1,
  pages = "1--11"
}

#350: D. K. Ufuktepe, F. Yang, Y. M. Kassim, H. Yu, R. J. Maude, K. Palaniappan, and S. Jaeger

Deep Learning-Based Cell Detection and Extraction in Thin Blood Smears for Malaria Diagnosis

IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1--6, 2021

malaria diagnosis, plasmodium falciparum, plas-modium vivax, microscopy, thin blood smear, machine learning, image analysis

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

Malaria is a major health threat caused by Plasmod-ium parasites that infect the red blood cells. Two predominant types of Plasmodium parasites are Plasmodium vivax (P. vivax) and Plasmodium falciparum (P. falciparum). Diagnosis of malaria typically involves visual microscopy examination of blood smears for malaria parasites. This is a tedious, error-prone visual inspection task requiring microscopy expertise which is often lacking in resource-poor settings. To address these problems, attempts have been made in recent years to automate malaria diagnosis using machine learning approaches. Several challenges need to be met for a machine learning approach to be successful in malaria diagnosis. Microscopy images acquired at different sites often vary in color, contrast, and consistency caused by different smear preparation and staining methods. Moreover, touching and overlapping cells complicate the red blood cell detection process, which can lead to inaccurate blood cell counts and thus incorrect parasitemia calculations. In this work, we propose a red blood cell detection and extraction framework to enable processing and analysis of single cells for follow-up processes like counting infected cells or identifying parasite species in thin blood smears. This framework consists of two modules: a cell detection module and a cell extraction module. The cell detection module trains a modified Channel-wise Feature Pyramid Network for Medicine (CFPNet-M) deep learning network that takes the green channel of the image and the color-deconvolution processed image as inputs, and learns a truncated distance transform image of cell annotations. CFPNet-M is chosen due to its low resource requirements, while the distance transform allows achieving more accurate cell counts for dense cells. Once the cells are detected by the network, the cell extraction module is used to extract single cells from the original image and count the number of cells. Our preliminary results based on 193 patients (including 148 P. Falciparum infected patients, and 45 uninfected patients) show that our framework achieves cell count accuracy of 92.2%.

@inproceedings{Ufuktepe2021deep-malaria,
  author = "D. K. Ufuktepe and F. Yang and Y. M. Kassim and H. Yu and R. J. Maude and K. Palaniappan and S. Jaeger",
  title = "Deep Learning-Based Cell Detection and Extraction in Thin Blood Smears for Malaria Diagnosis",
  year = 2021,
  journal = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  pages = "1--6",
  keywords = "malaria diagnosis, plasmodium falciparum, plas-modium vivax, microscopy, thin blood smear, machine learning, image analysis",
  doi = "10.1109/AIPR52630.2021.9762109"
}

D. K. Ufuktepe, F. Yang, Y. M. Kassim, H. Yu, R. J. Maude, K. Palaniappan, and S. Jaeger. Deep Learning-Based Cell Detection and Extraction in Thin Blood Smears for Malaria Diagnosis. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1--6, 2021.

#349: C. Davis, J. Collins, J. Fraser, H. Zhang, S. Yao, E. Lattanzio, B. Balakrishnan, Y. Duan, P. Calyam, and K. Palaniappan

3D Modeling of Cities for Virtual Environments

2021 IEEE International Conference on Big Data (Big Data), pgs. 5587--5596, 2021

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{davis20213d,
  author = "C. Davis and J. Collins and J. Fraser and H. Zhang and S. Yao and E. Lattanzio and B. Balakrishnan and Y. Duan and P. Calyam and K. Palaniappan",
  title = "3D Modeling of Cities for Virtual Environments",
  year = 2021,
  booktitle = "2021 IEEE International Conference on Big Data (Big Data)",
  pages = "5587--5596"
}

C. Davis, J. Collins, J. Fraser, H. Zhang, S. Yao, E. Lattanzio, B. Balakrishnan, Y. Duan, P. Calyam, and K. Palaniappan. 3D Modeling of Cities for Virtual Environments. 2021 IEEE International Conference on Big Data (Big Data), pages 5587--5596, 2021.

#348: G. Rahmon, F. Bunyak, G. Seetharaman, and K. Palaniappan

Motion U-Net: Multi-cue Encoder-Decoder Network for Motion Segmentation

2020 25th International Conference on Pattern Recognition (ICPR), pgs. 8125-8132, 2021

moving object, change detection, background subtraction, flux tensor, convolutional neural networks, u-net

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{2021a,
  author = "G. Rahmon and F. Bunyak and G. Seetharaman and K. Palaniappan",
  title = "Motion U-Net: Multi-cue Encoder-Decoder Network for Motion Segmentation",
  year = 2021,
  journal = "2020 25th International Conference on Pattern Recognition (ICPR)",
  pages = "8125-8132",
  keywords = "moving object, change detection, background subtraction, flux tensor, convolutional neural networks, u-net",
  doi = "10.1109/ICPR48806.2021.9413211"
}

G. Rahmon, F. Bunyak, G. Seetharaman, and K. Palaniappan. Motion U-Net: Multi-cue Encoder-Decoder Network for Motion Segmentation. 2020 25th International Conference on Pattern Recognition (ICPR), pages 8125-8132, 2021.

#347: Y. Y. Wang, O. V. Glinskii, F. Bunyak, and K. Palaniappan

Ensemble of Deep Learning Cascades for Segmentation of Blood Vessels in Confocal Microscopy Images

5OTH ANNUAL APPLIED IMAGERY PATTERN RECOGNITION WORKSHOP, 2021

vessel segmentation, confocal microscopy images, deep learning, semantic segmentation

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{2021a,
  author = "Y. Y. Wang and O. V. Glinskii and F. Bunyak and K. Palaniappan",
  title = "Ensemble of Deep Learning Cascades for Segmentation of Blood Vessels in Confocal Microscopy Images",
  year = 2021,
  booktitle = "5OTH ANNUAL APPLIED IMAGERY PATTERN RECOGNITION WORKSHOP",
  keywords = "vessel segmentation, confocal microscopy images, deep learning, semantic segmentation"
}

Y. Y. Wang, O. V. Glinskii, F. Bunyak, and K. Palaniappan. Ensemble of Deep Learning Cascades for Segmentation of Blood Vessels in Confocal Microscopy Images. 5OTH ANNUAL APPLIED IMAGERY PATTERN RECOGNITION WORKSHOP, 2021.

#345: E. Asante, D. Hummel, S. Gurung, Y. Kassim, N. Al-Shakarji, K. Palaniappan, V. Sittaramane, and A. Chandrasekhar

Defective neuronal positioning correlates with aberrant motor circuit function in zebrafish

Frontiers in Neural Circuits, Volume 15, 2021

jaw movement, food intake, behavior, neuronal migration, axon guidance, zebrafish, neural circuits, facial branchiomotor neuron, fusion, biomedical

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@article{Zebafish2021:FrontiersJornal,
  author = "E. Asante and D. Hummel and S. Gurung and Y. Kassim and N. Al-Shakarji and K. Palaniappan and V. Sittaramane and A. Chandrasekhar",
  title = "Defective neuronal positioning correlates with aberrant motor circuit function in zebrafish",
  year = 2021,
  journal = "Frontiers in Neural Circuits",
  volume = 15,
  keywords = "jaw movement, food intake, behavior, neuronal migration, axon guidance, zebrafish, neural circuits, facial branchiomotor neuron, fusion, biomedical",
  doi = "10.3389/fncir.2021.690475",
  url = "https://www.frontiersin.org/article/10.3389/fncir.2021.690475"
}

E. Asante, D. Hummel, S. Gurung, Y. Kassim, N. Al-Shakarji, K. Palaniappan, V. Sittaramane, and A. Chandrasekhar. Defective neuronal positioning correlates with aberrant motor circuit function in zebrafish. Frontiers in Neural Circuits, volume 15, 2021.

#344: G. Koerner, R. Surya, K. Palaniappan, P. Calyam, F. Bunyak, and M. R. Maschmann

In-situ scanning electron microscope chemical vapor deposition as a platform for nanomanufacturing insights

ASME International Mechanical Engineering Congress and Exposition, Volume 85567, pgs. V02BT02A052, 2021

Bibtex, PlainText, Google Scholar

@inproceedings{CNT:ASME2021,
  author = "G. Koerner and R. Surya and K. Palaniappan and P. Calyam and F. Bunyak and M. R. Maschmann",
  title = "In-situ scanning electron microscope chemical vapor deposition as a platform for nanomanufacturing insights",
  year = 2021,
  booktitle = "ASME International Mechanical Engineering Congress and Exposition",
  volume = 85567,
  pages = "V02BT02A052"
}

#343: R. Bao, N. Al-Shakarji, F. Bunyak, and K. Palaniappan

DMNet: Dual-stream marker guided deep network for dense cell segmentation and lineage tracking

IEEE International Conference on Computer Vision Workshops (ICCVW), pgs. 3354-3363, 2021

training, image segmentation, shape, microscopy, stem cells, cell detection, cell tracking, mitosis

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{RinaNoor2021:ICCV_MDNet,
  author = "R. Bao and N. Al-Shakarji and F. Bunyak and K. Palaniappan",
  title = "DMNet: Dual-stream marker guided deep network for dense cell segmentation and lineage tracking",
  year = 2021,
  booktitle = "IEEE International Conference on Computer Vision Workshops (ICCVW)",
  pages = "3354-3363",
  keywords = "training, image segmentation, shape, microscopy, stem cells, cell detection, cell tracking, mitosis",
  doi = "10.1109/ICCVW54120.2021.00375",
  url = "https://ieeexplore.ieee.org/document/9607707"
}

R. Bao, N. Al-Shakarji, F. Bunyak, and K. Palaniappan. DMNet: Dual-stream marker guided deep network for dense cell segmentation and lineage tracking. IEEE International Conference on Computer Vision Workshops (ICCVW), pages 3354-3363, 2021.

#339: C. Qu, P. Calyam, J. Yu, A. Vandanapu, O. Opeoluwa, K. Gao, S. Wang, R. Chastain, and K. Palaniappan

DroneCOCoNet: Learning-based edge computation offloading and control networking for drone video analytics

Future Generation Computer Systems, Volume 125, pgs. 247-262, 2021

edge/fog computation offloading, drone video analytics, mobile edge computing, learning-based scheme, data processing in fog computing

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@article{Qu2021:FGCS_DroneCOCoNet,
  author = "C. Qu and P. Calyam and J. Yu and A. Vandanapu and O. Opeoluwa and K. Gao and S. Wang and R. Chastain and K. Palaniappan",
  title = "DroneCOCoNet: Learning-based edge computation offloading and control networking for drone video analytics",
  year = 2021,
  journal = "Future Generation Computer Systems",
  volume = 125,
  pages = "247-262",
  keywords = "edge/fog computation offloading, drone video analytics, mobile edge computing, learning-based scheme, data processing in fog computing",
  doi = "https://doi.org/10.1016/j.future.2021.06.040",
  url = "https://www.sciencedirect.com/science/article/pii/S0167739X21002351"
}

C. Qu, P. Calyam, J. Yu, A. Vandanapu, O. Opeoluwa, K. Gao, S. Wang, R. Chastain, and K. Palaniappan. DroneCOCoNet: Learning-based edge computation offloading and control networking for drone video analytics. Future Generation Computer Systems, volume 125, pages 247-262, 2021.

#338: S. Yao, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan

City-Scale Point Cloud Stitching Using 2D/3D Registration for Large Geographical Coverage

International Conference on Pattern Recognition, pgs. 32--45, 2021

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{yao2021-city-scale-stitching,
  author = "S. Yao and H. AliAkbarpour and G. Seetharaman and K. Palaniappan",
  title = "City-Scale Point Cloud Stitching Using 2D/3D Registration for Large Geographical Coverage",
  year = 2021,
  booktitle = "International Conference on Pattern Recognition",
  pages = "32--45"
}

S. Yao, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan. City-Scale Point Cloud Stitching Using 2D/3D Registration for Large Geographical Coverage. International Conference on Pattern Recognition, pages 32--45, 2021.

#356: H. AliAkbarpour, K. Gao, R. Aktar, S. Suddarth, and K. Palaniappan

Structure from motion and mosaicking for high-throughput field-scale phenotyping

High-Throughput Crop Phenotyping, Springer International Publishing, pgs. 55--69, 2021

structure-from-motion (sfm), video stabilization, image registration, orthorectification, mosaicking

Abstract, Bibtex, PlainText, URL, DOI, Google Scholar

@inbook{AliAkbarpour_BSfM_Bookchapter_2021,
  author = "H. AliAkbarpour and K. Gao and R. Aktar and S. Suddarth and K. Palaniappan",
  title = "Structure from motion and mosaicking for high-throughput field-scale phenotyping",
  year = 2021,
  booktitle = "High-Throughput Crop Phenotyping",
  publisher = "Springer International Publishing",
  pages = "55--69",
  keywords = "structure-from-motion (sfm), video stabilization, image registration, orthorectification, mosaicking",
  doi = "10.1007/978-3-030-73734-4_4",
  url = "https://doi.org/10.1007/978-3-030-73734-4_4"
}

H. AliAkbarpour, K. Gao, R. Aktar, S. Suddarth, and K. Palaniappan. Structure from motion and mosaicking for high-throughput field-scale phenotyping. High-Throughput Crop Phenotyping, Springer International Publishing, pages 55--69, 2021.

#337: N. Al-Shakarji, K. Gao, F. Bunyak, H. Aliakbarpour, E. Blasch, P. Narayaran, G. Seetharaman, and K. Palaniappan

Impact of georegistration accuracy on wide area motion imagery object detection and tracking

IEEE International Conference on Information Fusion (FUSION), pgs. 1-8, 2021

deep learning, fusion, wami, object detection, multi-object tracking, dod

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{Noor2021:Fusion_ImpactGeoReg,
  author = "N. Al-Shakarji and K. Gao and F. Bunyak and H. Aliakbarpour and E. Blasch and P. Narayaran and G. Seetharaman and K. Palaniappan",
  title = "Impact of georegistration accuracy on wide area motion imagery object detection and tracking",
  year = 2021,
  booktitle = "IEEE International Conference on Information Fusion (FUSION)",
  pages = "1-8",
  keywords = "deep learning, fusion, wami, object detection, multi-object tracking, dod",
  url = "https://ieeexplore.ieee.org/document/9626982"
}

N. Al-Shakarji, K. Gao, F. Bunyak, H. Aliakbarpour, E. Blasch, P. Narayaran, G. Seetharaman, and K. Palaniappan. Impact of georegistration accuracy on wide area motion imagery object detection and tracking. IEEE International Conference on Information Fusion (FUSION), pages 1-8, 2021.

#331: K. Gao, H. Aliakbarpour, J. Fraser, K. Nouduri, F. Bunyak, R. Massaro, G. Seetharaman, and K. Palaniappan

Local feature performance evaluation for Structure-from-Motion and multi-view stereo using simulated city-scale aerial imagery

IEEE Sensors Journal, Volume 21, pgs. 11615 - 11627, 2020

local feature detector, sift, surf, orb, akaze binary feature, mser, lf-net, superpoint, image matching, photogrammetry, camera pose, bundle adjustment, simulation environments, virtual reality, augmented reality

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@article{KeGao2020:Sensors_SfM_COLMAP,
  author = "K. Gao and H. Aliakbarpour and J. Fraser and K. Nouduri and F. Bunyak and R. Massaro and G. Seetharaman and K. Palaniappan",
  title = "Local feature performance evaluation for Structure-from-Motion and multi-view stereo using simulated city-scale aerial imagery",
  year = 2020,
  journal = "IEEE Sensors Journal",
  volume = 21,
  number = 10,
  pages = " 11615 - 11627",
  month = "Dec",
  keywords = "local feature detector, sift, surf, orb, akaze binary feature, mser, lf-net, superpoint, image matching, photogrammetry, camera pose, bundle adjustment, simulation environments, virtual reality, augmented reality",
  doi = "10.1109/JSEN.2020.3042810",
  url = "https://ieeexplore.ieee.org/abstract/document/9284524"
}

K. Gao, H. Aliakbarpour, J. Fraser, K. Nouduri, F. Bunyak, R. Massaro, G. Seetharaman, and K. Palaniappan. Local feature performance evaluation for Structure-from-Motion and multi-view stereo using simulated city-scale aerial imagery. IEEE Sensors Journal, volume 21, issue 10, pages  11615 - 11627, December 2020.

#333: J. K. Lewis, I. E. Toubal, H. Chen, V. Sandesera, M. Lomnitz, Z. Hampel-Arias, C. Prasad, and K. Palaniappan

Deepfake Video Detection Based on Spatial, Spectral, and Temporal Inconsistencies Using Multimodal Deep Learning

Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1-9, 2020

deepfake detection, deep learning, multi-modal, computer vision

Abstract, Bibtex, PlainText, Software, PDF, URL, DOI, Google Scholar

@inproceedings{@INPROCEEDINGS{9425167,  author={Lewis, John K. and Toubal, Imad Eddine and Chen, Helen and Sandesera, Vishal and Lomnitz, Michael and Hampel-Arias, Zigfried and Prasad, Calyam and Palaniappan, Kannappan},  booktitle={2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)},   title={Deepfake Video Detection Based on Spatial, Spectral, and Temporal Inconsistencies Using Multimodal Deep Learning},   year={2020},  volume={},  number={},  pages={1-9},  doi={10.1109/AIPR50011.2020.9425167}},
  author = "J. K. Lewis and I. E. Toubal and H. Chen and V. Sandesera and M. Lomnitz and Z. Hampel-Arias and C. Prasad and K. Palaniappan",
  title = "Deepfake Video Detection Based on Spatial, Spectral, and Temporal Inconsistencies Using Multimodal Deep Learning",
  year = 2020,
  journal = "Applied Imagery Pattern Recognition Workshop (AIPR)",
  publisher = "IEEE",
  pages = "1-9",
  month = "Oct",
  keywords = "deepfake detection, deep learning, multi-modal, computer vision",
  doi = "10.1109/AIPR50011.2020.9425167",
  url = "https://ieeexplore.ieee.org/abstract/document/9425167"
}

J. K. Lewis, I. E. Toubal, H. Chen, V. Sandesera, M. Lomnitz, Z. Hampel-Arias, C. Prasad, and K. Palaniappan. Deepfake Video Detection Based on Spatial, Spectral, and Temporal Inconsistencies Using Multimodal Deep Learning. Applied Imagery Pattern Recognition Workshop (AIPR), IEEE, pages 1-9, October 2020.

#334: K. Gao, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan

DCT-based local descriptor for robust matching and feature tracking in wide area motion imagery

IEEE Geoscience and Remote Sensing Letters, pgs. 1-5, 2020

3d stereo, aerial video, convolutional neural network, cnn, dct, feature descriptor, matching, point correspondences, spatial frequency

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@article{Gao2020:GRSL_DCTF,
  author = "K. Gao and H. AliAkbarpour and G. Seetharaman and K. Palaniappan",
  title = "DCT-based local descriptor for robust matching and feature tracking in wide area motion imagery",
  year = 2020,
  journal = "IEEE Geoscience and Remote Sensing Letters",
  pages = "1-5",
  month = "Jun",
  keywords = "3d stereo, aerial video, convolutional neural network, cnn, dct, feature descriptor, matching, point correspondences, spatial frequency",
  doi = "10.1109/LGRS.2020.3000762",
  url = "https://ieeexplore.ieee.org/abstract/document/9126851"
}

K. Gao, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan. DCT-based local descriptor for robust matching and feature tracking in wide area motion imagery. IEEE Geoscience and Remote Sensing Letters, pages 1-5, June 2020.

#332: A. Nafis, E. Teters, R. Aktar, G. Seetharaman, and K. Palaniappan

GPU and multi-threaded CPU enabled normalized cross correlation

Proc. SPIE Conf. Geospatial InfoFusion X (Defense + Commercial Sensing), 2020

normalized cross correlation, multi-threading, gpu

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Nafis:NCC-2020,
  author = "A. Nafis and E. Teters and R. Aktar and G. Seetharaman and K. Palaniappan",
  title = "GPU and multi-threaded CPU enabled normalized cross correlation",
  year = 2020,
  journal = "Proc. SPIE Conf. Geospatial InfoFusion X (Defense + Commercial Sensing)",
  month = "Apr",
  keywords = "normalized cross correlation, multi-threading, gpu",
  doi = "10.1117/12.2561529",
  url = "https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11398/1139805/GPU-and-multi-threaded-CPU-enabled-normalized-cross-correlation/10.1117/12.2561529.short?SSO=1"
}

A. Nafis, E. Teters, R. Aktar, G. Seetharaman, and K. Palaniappan. GPU and multi-threaded CPU enabled normalized cross correlation. Proc. SPIE Conf. Geospatial InfoFusion X (Defense + Commercial Sensing), April 2020.

#326: O. Glinskii, V. Huxley, L. Xie, F. Bunyak, K. Palaniappan, and V. Glinsky

Microvascular Pachymeningeal Lymphatic Structures: Association with Blood Vessels and Cranial Bones

The FASEB Journal, Volume 34, pgs. 1--1, 2020

Bibtex, PlainText, Google Scholar

@article{Glinskii:FASEB2020,
  author = "O. Glinskii and V. Huxley and L. Xie and F. Bunyak and K. Palaniappan and V. Glinsky",
  title = "Microvascular Pachymeningeal Lymphatic Structures: Association with Blood Vessels and Cranial Bones",
  year = 2020,
  journal = "The FASEB Journal",
  publisher = "Wiley Online Library",
  volume = 34,
  number = S1,
  pages = "1--1"
}

#325: D. E. Kharismawati, H. A. Akbarpour, R. Aktar, F. Bunyak, K. Palaniappan, and T. Kazic

CorNet: Unsupervised Deep Homography Estimation for Agricultural Aerial Imagery

European Conference on Computer Vision Workshops (ECCVW), Springer International Publishing, pgs. 400--417, 2020

Bibtex, PlainText, Google Scholar

@inproceedings{Dewi:ECCVW2020,
  author = "D. E. Kharismawati and H. A. Akbarpour and R. Aktar and F. Bunyak and K. Palaniappan and T. Kazic",
  title = "CorNet: Unsupervised Deep Homography Estimation for Agricultural Aerial Imagery",
  year = 2020,
  booktitle = "European Conference on Computer Vision Workshops (ECCVW)",
  publisher = "Springer International Publishing",
  pages = "400--417"
}

#323: R. Aktar, V. H. Huxley, G. Guidoboni, H. AliAkbarpour, F. Bunyak, and K. Palaniappan

Mosaicing of Dynamic Mesentery Video with Gradient Blending

IEEE International Conference on Image Processing (ICIP), pgs. 563-567, 2020

Bibtex, PlainText, DOI, Google Scholar

@inproceedings{Aktar:ICIP2020,
  author = "R. Aktar and V. H. Huxley and G. Guidoboni and H. AliAkbarpour and F. Bunyak and K. Palaniappan",
  title = "Mosaicing of Dynamic Mesentery Video with Gradient Blending",
  year = 2020,
  booktitle = "IEEE International Conference on Image Processing (ICIP)",
  pages = "563-567",
  doi = "10.1109/ICIP40778.2020.9191045"
}

#322: A. E. Morel, D. K. Ufuktepe, R. Ignatowicz, A. Riddle, C. Qu, P. Calyam, and K. Palaniappan

Enhancing Network-edge Connectivity and Computation Security in Drone Video Analytics

Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1--12, 2020

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{Morel2020enhancing,
  author = "A. E. Morel and D. K. Ufuktepe and R. Ignatowicz and A. Riddle and C. Qu and P. Calyam and K. Palaniappan",
  title = "Enhancing Network-edge Connectivity and Computation Security in Drone Video Analytics",
  year = 2020,
  booktitle = "Applied Imagery Pattern Recognition Workshop (AIPR)",
  pages = "1--12"
}

A. E. Morel, D. K. Ufuktepe, R. Ignatowicz, A. Riddle, C. Qu, P. Calyam, and K. Palaniappan. Enhancing Network-edge Connectivity and Computation Security in Drone Video Analytics. Applied Imagery Pattern Recognition Workshop (AIPR), pages 1--12, 2020.

#321: D. K. Ufuktepe, J. Collins, H. AliAkbarpour, P. Narayanan, G. Seetharaman, and K. Palaniappan

Short-term video stabilization using ground plane segmentation for low altitude UAV object tracking (conference presentation)

Geospatial Informatics X, Volume 11398, pgs. 113980B, 2020

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{Ufuktepe2020short,
  author = "D. K. Ufuktepe and J. Collins and H. AliAkbarpour and P. Narayanan and G. Seetharaman and K. Palaniappan",
  title = "Short-term video stabilization using ground plane segmentation for low altitude UAV object tracking (conference presentation)",
  year = 2020,
  booktitle = "Geospatial Informatics X",
  volume = 11398,
  pages = "113980B"
}

D. K. Ufuktepe, J. Collins, H. AliAkbarpour, P. Narayanan, G. Seetharaman, and K. Palaniappan. Short-term video stabilization using ground plane segmentation for low altitude UAV object tracking (conference presentation). Geospatial Informatics X, volume 11398, pages 113980B, 2020.

#320: K. Nouduri, F. Bunyak, S. Yao, H. Aliakbarpour, S. Agarwal, R. Rao, and K. Palaniappan

Deep Learning Based Landmark Matching For Aerial Geolocalization

2020 IEEE International Conference on Image Processing (ICIP), pgs. 1911-1915, 2020

Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Nouduri2020deep,
  author = "K. Nouduri and F. Bunyak and S. Yao and H. Aliakbarpour and S. Agarwal and R. Rao and K. Palaniappan",
  title = "Deep Learning Based Landmark Matching For Aerial Geolocalization",
  year = 2020,
  booktitle = "2020 IEEE International Conference on Image Processing (ICIP)",
  pages = "1911-1915",
  doi = "10.1109/ICIP40778.2020.9191342"
}

#319: D. K. Ufuktepe, K. Palaniappan, M. Elmali, and T. I. Baskin

Rtip: A Fully Automated Root Tip Tracker For Measuring Plant Growth With Intermittent Perturbations

IEEE International Conference on Image Processing (ICIP), pgs. 2516--2520, 2020

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Ufuktepe2020rtip,
  author = "D. K. Ufuktepe and K. Palaniappan and M. Elmali and T. I. Baskin",
  title = "Rtip: A Fully Automated Root Tip Tracker For Measuring Plant Growth With Intermittent Perturbations",
  year = 2020,
  booktitle = "IEEE International Conference on Image Processing (ICIP)",
  pages = "2516--2520",
  doi = "10.1109/ICIP40778.2020.9191008"
}

D. K. Ufuktepe, K. Palaniappan, M. Elmali, and T. I. Baskin. Rtip: A Fully Automated Root Tip Tracker For Measuring Plant Growth With Intermittent Perturbations. IEEE International Conference on Image Processing (ICIP), pages 2516--2520, 2020.

#318: G. Rahmon, F. Bunyak, G. Seetharaman, and K. Palaniappan

Evaluation of Different Decision Fusion Mechanisms for Robust Moving Object Detection

2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1-7, 2020

Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Gani2020AIPR,
  author = "G. Rahmon and F. Bunyak and G. Seetharaman and K. Palaniappan",
  title = "Evaluation of Different Decision Fusion Mechanisms for Robust Moving Object Detection",
  year = 2020,
  booktitle = "2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  pages = "1-7",
  doi = "10.1109/AIPR50011.2020.9425078"
}

#316: H. Fan, D. Du, L. Wen, P. Zhu, Q. Hu, H. Ling, M. Shah, J. Pan, A. Schumann, B. Dong, D. Stadler, D. Xu, F. Bunyak, G. Seetharaman, G. Liu, V. Haritha, P. S. Hrishikesh, J. Han, K. Palaniappan, K. Zhu, L. W. Sommer, L. Zhang, L. Shine, M. Yao, N. Al-Shakarji, S. Li, T. Sun, W. Sai, W. Yu, X. Wu, X. Hong, X. Wei, X. Zhao, Y. Zhao, Y. Gong, Y. Yao, Y. He, Z. Zhao, Z. Xie, Z. Yang, Z. Xu, Z. Luo, and Z. Duan

VisDrone-MOT2020: The vision meets drone multiple object tracking challenge results

European Conference on Computer Vision (ECCV), Springer International Publishing, pgs. 713--727, 2020

drone-based multiple object tracking, drone performance evaluation, multi-object tracking

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Noor2020:ECCV_VisDrone_Challenge_report,
  author = "H. Fan and D. Du and L. Wen and P. Zhu and Q. Hu and H. Ling and M. Shah and J. Pan and A. Schumann and B. Dong and D. Stadler and D. Xu and F. Bunyak and G. Seetharaman and G. Liu and V. Haritha and P. S. Hrishikesh and J. Han and K. Palaniappan and K. Zhu and L. W. Sommer and L. Zhang and L. Shine and M. Yao and N. Al-Shakarji and S. Li and T. Sun and W. Sai and W. Yu and X. Wu and X. Hong and X. Wei and X. Zhao and Y. Zhao and Y. Gong and Y. Yao and Y. He and Z. Zhao and Z. Xie and Z. Yang and Z. Xu and Z. Luo and Z. Duan",
  title = "VisDrone-MOT2020: The vision meets drone multiple object tracking challenge results",
  year = 2020,
  booktitle = "European Conference on Computer Vision (ECCV)",
  publisher = "Springer International Publishing",
  pages = "713--727",
  keywords = "drone-based multiple object tracking, drone performance evaluation, multi-object tracking",
  doi = "10.1007/978-3-030-66823-5_43",
  url = "https://link.springer.com/chapter/10.1007/978-3-030-66823-5_43"
}

H. Fan, D. Du, L. Wen, P. Zhu, Q. Hu, H. Ling, M. Shah, J. Pan, A. Schumann, B. Dong, D. Stadler, D. Xu, F. Bunyak, G. Seetharaman, G. Liu, V. Haritha, P. S. Hrishikesh, J. Han, K. Palaniappan, K. Zhu, L. W. Sommer, L. Zhang, L. Shine, M. Yao, N. Al-Shakarji, S. Li, T. Sun, W. Sai, W. Yu, X. Wu, X. Hong, X. Wei, X. Zhao, Y. Zhao, Y. Gong, Y. Yao, Y. He, Z. Zhao, Z. Xie, Z. Yang, Z. Xu, Z. Luo, and Z. Duan. VisDrone-MOT2020: The vision meets drone multiple object tracking challenge results. European Conference on Computer Vision (ECCV), Springer International Publishing, pages 713--727, 2020.

#315: N. Al-Shakarji, E. Ufuktepe, F. Bunyak, H. Aliakbarpour, G. Seetharaman, and K. Palaniappan

Semi-automatic system for rapid annotation of moving objects in surveillance videos using deep detection and multi-object tracking techniques

IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1-6, 2020

ground truth generator, object annotation, deep learning, object detection, tracking

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Noor2020:AIPR_Video_annotation,
  author = "N. Al-Shakarji and E. Ufuktepe and F. Bunyak and H. Aliakbarpour and G. Seetharaman and K. Palaniappan",
  title = "Semi-automatic system for rapid annotation of moving objects in surveillance videos using deep detection and multi-object tracking techniques",
  year = 2020,
  booktitle = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  pages = "1-6",
  keywords = "ground truth generator, object annotation, deep learning, object detection, tracking",
  doi = "10.1109/AIPR50011.2020.9425339",
  url = "https://ieeexplore.ieee.org/abstract/document/9425339"
}

N. Al-Shakarji, E. Ufuktepe, F. Bunyak, H. Aliakbarpour, G. Seetharaman, and K. Palaniappan. Semi-automatic system for rapid annotation of moving objects in surveillance videos using deep detection and multi-object tracking techniques. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1-6, 2020.

#314: D. E. Kharismawati, H. A. Akbarpour, R. Aktar, F. Bunyak, K. Palaniappan, and T. Kazic

CorNet: unsupervised deep homography estimation for agricultural aerial imagery

ECCV 2020 Workshops, Volume LNCS 12540, 2020

unsupervised, convolutional neural network, homography estimation, uav freely flown, maize, mosaicking

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@article{2020a,
  author = "D. E. Kharismawati and H. A. Akbarpour and R. Aktar and F. Bunyak and K. Palaniappan and T. Kazic",
  title = "CorNet: unsupervised deep homography estimation for agricultural aerial imagery",
  year = 2020,
  journal = "ECCV 2020 Workshops",
  publisher = "Springer, Cham",
  volume = LNCS 12540,
  keywords = "unsupervised, convolutional neural network, homography estimation, uav freely flown, maize, mosaicking",
  doi = "10.1007/978-3-030-65414-6_28",
  url = "https://link.springer.com/chapter/10.1007/978-3-030-65414-6_28#citeas"
}

D. E. Kharismawati, H. A. Akbarpour, R. Aktar, F. Bunyak, K. Palaniappan, and T. Kazic. CorNet: unsupervised deep homography estimation for agricultural aerial imagery. ECCV 2020 Workshops, Springer, Cham, volume LNCS 12540, 2020.

#313: H. Aliakbarpour, K. Palaniappan, and G. Seetharaman

Stabilization of Airborne Video Using Sensor Exterior Orientation with Analytical Homography Modeling

Machine Vision and Navigation, Springer International Publishing, pgs. 579--595, 2020

video stabilization, image registration, georegistration, camera parameter optimization, homography, parallax, 3d

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inbook{Aliakbarpour_EVG_Stabilization_2020,
  author = "H. Aliakbarpour and K. Palaniappan and G. Seetharaman",
  title = "Stabilization of Airborne Video Using Sensor Exterior Orientation with Analytical Homography Modeling",
  year = 2020,
  booktitle = "Machine Vision and Navigation",
  publisher = "Springer International Publishing",
  pages = "579--595",
  keywords = "video stabilization, image registration, georegistration, camera parameter optimization, homography, parallax, 3d",
  doi = "10.1007/978-3-030-22587-2_17",
  url = "https://doi.org/10.1007/978-3-030-22587-2_17"
}

H. Aliakbarpour, K. Palaniappan, and G. Seetharaman. Stabilization of Airborne Video Using Sensor Exterior Orientation with Analytical Homography Modeling. Machine Vision and Navigation, Springer International Publishing, pages 579--595, 2020.

#312: R. Aktar, V. H. Huxley, G. Guidoboni, O. V. Glinskii, and K. Palaniappan

Interactive global mosaic stitching from mesentery video sequences

IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1-5, 2020

global mosaicing, registration, normalize cross correlation, fuse descriptor, mesentery

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{9425109,
  author = "R. Aktar and V. H. Huxley and G. Guidoboni and O. V. Glinskii and K. Palaniappan",
  title = "Interactive global mosaic stitching from mesentery video sequences",
  year = 2020,
  booktitle = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  pages = "1-5",
  keywords = "global mosaicing, registration, normalize cross correlation, fuse descriptor, mesentery",
  doi = "10.1109/AIPR50011.2020.9425109",
  url = "https://ieeexplore.ieee.org/abstract/document/9425109"
}

R. Aktar, V. H. Huxley, G. Guidoboni, O. V. Glinskii, and K. Palaniappan. Interactive global mosaic stitching from mesentery video sequences. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1-5, 2020.

#311: R. Aktar, D. E. Kharismawati, K. Palaniappan, H. Aliakbarpour, F. Bunyak, A. E. Stapleton, and T. Kazic

Robust mosaicking of maize fields from aerial imagery

Applications in Plant Sciences, Volume 8, pgs. e11387, 2020

aerial imagery, crop field imagery, maize, mosaicking, small unmanned aerial system (suas), video summarization

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@article{https://doi.org/10.1002/aps3.11387,
  author = "R. Aktar and D. E. Kharismawati and K. Palaniappan and H. Aliakbarpour and F. Bunyak and A. E. Stapleton and T. Kazic",
  title = "Robust mosaicking of maize fields from aerial imagery",
  year = 2020,
  journal = "Applications in Plant Sciences",
  volume = 8,
  number = 8,
  pages = "e11387",
  keywords = "aerial imagery, crop field imagery, maize, mosaicking, small unmanned aerial system (suas), video summarization",
  doi = "https://doi.org/10.1002/aps3.11387",
  url = "https://bsapubs.onlinelibrary.wiley.com/doi/abs/10.1002/aps3.11387"
}

R. Aktar, D. E. Kharismawati, K. Palaniappan, H. Aliakbarpour, F. Bunyak, A. E. Stapleton, and T. Kazic. Robust mosaicking of maize fields from aerial imagery. Applications in Plant Sciences, volume 8, issue 8, pages e11387, 2020.

#310: S. Yao, Y. Wang, H. AliAkbarpour, G. Seetharaman, R. Rao, and K. Palaniappan

SMVNet: Deep Learning Architectures for Accurate and Robust Multi-View Stereopsis

2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1--6, 2020

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{yao2020smvnet,
  author = "S. Yao and Y. Wang and H. AliAkbarpour and G. Seetharaman and R. Rao and K. Palaniappan",
  title = "SMVNet: Deep Learning Architectures for Accurate and Robust Multi-View Stereopsis",
  year = 2020,
  booktitle = "2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  pages = "1--6"
}

S. Yao, Y. Wang, H. AliAkbarpour, G. Seetharaman, R. Rao, and K. Palaniappan. SMVNet: Deep Learning Architectures for Accurate and Robust Multi-View Stereopsis. 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1--6, 2020.

#309: R. R. Ramisetty, C. Qu, R. Aktar, S. Wang, P. Calyam, and K. Palaniappan

Dynamic computation off-loading and control based on occlusion detection in drone video analytics

Proceedings of the 21st International Conference on Distributed Computing and Networking, Association for Computing Machinery, 2020

drone video analytics, occlusion detection, multimedia networking protocols, computation off-loading, network management

Abstract, Bibtex, PlainText, URL, DOI, Google Scholar

@inproceedings{10.1145/3369740.3369793,
  author = "R. R. Ramisetty and C. Qu and R. Aktar and S. Wang and P. Calyam and K. Palaniappan",
  title = "Dynamic computation off-loading and control based on occlusion detection in drone video analytics",
  year = 2020,
  booktitle = "Proceedings of the 21st International Conference on Distributed Computing and Networking",
  publisher = "Association for Computing Machinery",
  keywords = "drone video analytics, occlusion detection, multimedia networking protocols, computation off-loading, network management",
  doi = "10.1145/3369740.3369793",
  url = "https://doi.org/10.1145/3369740.3369793"
}

R. R. Ramisetty, C. Qu, R. Aktar, S. Wang, P. Calyam, and K. Palaniappan. Dynamic computation off-loading and control based on occlusion detection in drone video analytics. Proceedings of the 21st International Conference on Distributed Computing and Networking, Association for Computing Machinery, 2020.

#308: R. Aktar, V. H. Huxley, G. Guidoboni, H. AliAkbarpour, F. Bunyak, and K. Palaniappan

Mosaicing of dynamic mesentery video with gradient blending

IEEE International Conference on Image Processing (ICIP), pgs. 563--567, 2020

normalize cross correlation, gradient blending, mosaicing, registration, biomedical, mesentery

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{aktar2020mosaicing,
  author = "R. Aktar and V. H. Huxley and G. Guidoboni and H. AliAkbarpour and F. Bunyak and K. Palaniappan",
  title = "Mosaicing of dynamic mesentery video with gradient blending",
  year = 2020,
  journal = "IEEE International Conference on Image Processing (ICIP)",
  pages = "563--567",
  keywords = "normalize cross correlation, gradient blending, mosaicing, registration, biomedical, mesentery",
  doi = "10.1109/ICIP40778.2020.9191045",
  url = "https://ieeexplore.ieee.org/abstract/document/9191045"
}

R. Aktar, V. H. Huxley, G. Guidoboni, H. AliAkbarpour, F. Bunyak, and K. Palaniappan. Mosaicing of dynamic mesentery video with gradient blending. IEEE International Conference on Image Processing (ICIP), pages 563--567, 2020.

#335: Y. M. Kassim, K. Palaniappan, F. Yang, M. Poostchi, N. Palaniappan, R. J. Maude, S. Antani, and S. Jaeger

Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears

2020

Bibtex, PlainText, Software, URL, Google Scholar

@article{2020YasminCluster,
  author = "Y. M. Kassim and K. Palaniappan and F. Yang and M. Poostchi and N. Palaniappan and R. J. Maude and S. Antani and S. Jaeger",
  title = "Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears",
  year = 2020,
  url = "https://ieeexplore.ieee.org/document/9244549"
}

#336: E. Ufuktepe, V. Ramtekkar, K. Gao, N. Al-Shakarji, J. Fraser, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan

pyTAG: Python-based interactive training data generation for visual tracking algorithms

Proc. SPIE Conf. Geospatial InfoFusion X (Defense + Commercial Sensing), Volume 11398, 2020

ground-truth generation, visual tracking, crowd sourcing, annotation, deep learning

Abstract, Bibtex, PlainText, Software, URL, DOI, Google Scholar

Visual object tracking has been always an important topic in the computer vision community due to its wide range of applications in many domains. While a considerable number of unsupervised and supervised visual tracking algorithms have been developed, the visual tracking field continues to explore improved algorithms and challenging new applications such as multispectral object tracking, multiobject tracking and tracking from moving platforms. Ground truth-based evaluation of tracking algorithms is an essential component for measuring tracker performance. However, manual ground-truth generation by tagging objects of interest in video sequences is an extremely tedious and error prone task that can be subjective especially in complex scenes. In visual tracking, some of the common challenges based on the environment characteristics and object movements are occlusion of the objects of interest, splitting or merging of groups of objects, id switches, and target drift due to model update errors during tracking. These problems can cause video object tracking algorithms to lose the object of interest or start tracking the wrong object due to id switches between objects of similar appearance or target drift due to model update errors. To improve longer, persistent, more accurate tracking for intelligent scene perception, the new generation of tracking algorithms incorporate machine learning based approaches. These supervised tracking algorithms require large training sets, especially deep convolution neural networks with millions of parameters. Therefore, it is important to generate accurate training data and ground truth for tracking. In this study, a training data and ground-truth generation tool called pyTAG is implemented for visual tracking. The proposed tool's plugin structure allows integration, testing, and validation of different trackers. The tracker can be paused, resumed, forwarded, rewound and re-initialized after it loses the object during training data generation. Most importantly pyTAG allows users to change the object tracking method during ground-truth generation. This feature provides the flexibility to adapt the challenges that occur in ground-truth generation by switching the object tracking method that performs better than the other object tracking methods. This tool has been implemented to assist researchers to rapidly generate ground-truth and training data, fix annotations, run and visualize custom or existing object tracking techniques.

@inproceedings{ufuktepe2020pytag,
  author = "E. Ufuktepe and V. Ramtekkar and K. Gao and N. Al-Shakarji and J. Fraser and H. AliAkbarpour and G. Seetharaman and K. Palaniappan",
  title = " pyTAG: Python-based interactive training data generation for visual tracking algorithms",
  year = 2020,
  journal = "Proc. SPIE Conf. Geospatial InfoFusion X (Defense + Commercial Sensing)",
  volume = 11398,
  keywords = "ground-truth generation, visual tracking, crowd sourcing, annotation, deep learning",
  doi = "10.1117/12.2561718",
  url = "https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11398/113980D/pyTAG--python-based-interactive-training-data-generation-for-visual/10.1117/12.2561718.full?SSO=1"
}

E. Ufuktepe, V. Ramtekkar, K. Gao, N. Al-Shakarji, J. Fraser, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan.  pyTAG: Python-based interactive training data generation for visual tracking algorithms. Proc. SPIE Conf. Geospatial InfoFusion X (Defense + Commercial Sensing), volume 11398, 2020.

#303: V. Prasath, R. Pelapur, G. Seetharaman, and K. Palaniappan

Multiscale Structure Tensor for Improved Feature Extraction and Image Regularization

IEEE Transactions on Image Processing, Volume 28, pgs. 6198-6210, 2019

image restoration, structure tensor, variable exponent, anisotropic diffusion

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@article{2019a,
  author = "V. Prasath and R. Pelapur and G. Seetharaman and K. Palaniappan",
  title = "Multiscale Structure Tensor for Improved Feature Extraction and Image Regularization",
  year = 2019,
  journal = "IEEE Transactions on Image Processing",
  publisher = "IEEE",
  volume = 28,
  number = 12,
  pages = "6198-6210",
  month = "Dec",
  keywords = "image restoration, structure tensor, variable exponent, anisotropic diffusion",
  doi = "10.1109/TIP.2019.2924799",
  url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=Multiscale+Structure+Tensor+for+Improved+Feature+Extraction+and+Image+Regularization&btnG="
}

V. Prasath, R. Pelapur, G. Seetharaman, and K. Palaniappan. Multiscale Structure Tensor for Improved Feature Extraction and Image Regularization. IEEE Transactions on Image Processing, IEEE, volume 28, issue 12, pages 6198-6210, December 2019.

#302: K. Gao, S. Yao, H. AliAkbarpour, S. Agarwal, G. Seetharaman, and K. Palaniappan

Sensitivity of multiview 3D point cloud reconstruction to compression quality and image feature detectability

IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1-6, 2019

jpeg compression, feature detection, 3d reconstruction, evaluation

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{KeGao2019:AIPR_Sensitivity-3d-compression,
  author = "K. Gao and S. Yao and H. AliAkbarpour and S. Agarwal and G. Seetharaman and K. Palaniappan",
  title = "Sensitivity of multiview 3D point cloud reconstruction to compression quality and image feature detectability",
  year = 2019,
  journal = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  pages = "1-6",
  month = "Oct",
  keywords = "jpeg compression, feature detection, 3d reconstruction, evaluation",
  doi = "10.1109/AIPR47015.2019.9174580",
  url = "https://ieeexplore.ieee.org/document/9174580"
}

K. Gao, S. Yao, H. AliAkbarpour, S. Agarwal, G. Seetharaman, and K. Palaniappan. Sensitivity of multiview 3D point cloud reconstruction to compression quality and image feature detectability. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1-6, October 2019.

#307: O. V. Glinskii, V. H. Huxley, L. Xie, F. Bunyak, K. Palaniappan, and V. V. Glinsky

Complex non-sinus-associated pachymeningeal lymphatic structures: Interrelationship with blood microvasculature

Frontiers in physiology, Volume 10, pgs. 1-8, 2019

dura mater, lymphatics, vascular structures, microvessels, pachymeningeal

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@article{2019a,
  author = "O. V. Glinskii and V. H. Huxley and L. Xie and F. Bunyak and K. Palaniappan and V. V. Glinsky",
  title = "Complex non-sinus-associated pachymeningeal lymphatic structures: Interrelationship with blood microvasculature",
  year = 2019,
  journal = "Frontiers in physiology",
  publisher = "Frontiers",
  volume = 10,
  pages = "1-8",
  month = "Oct",
  keywords = "dura mater, lymphatics, vascular structures, microvessels, pachymeningeal",
  doi = "10.3389/fphys.2019.01364",
  url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=Complex+Non-sinus-associated+Pachymeningeal+Lymphatic+Structures%3A+Interrelationship+With+Blood+Microvasculature&btnG="
}

O. V. Glinskii, V. H. Huxley, L. Xie, F. Bunyak, K. Palaniappan, and V. V. Glinsky. Complex non-sinus-associated pachymeningeal lymphatic structures: Interrelationship with blood microvasculature. Frontiers in physiology, Frontiers, volume 10, pages 1-8, October 2019.

#306: Y. M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, and K. Palaniappan

Deep U-Net Regression and Hand-Crafted Feature Fusion for Accurate Blood Vessel Segmentation

2019 IEEE International Conference on Image Processing (ICIP), pgs. 1445-1449, 2019

semantic vessel segmentation, deep learning, histogram equalization, random forests, u-net

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{2019a,
  author = "Y. M. Kassim and O. V. Glinskii and V. V. Glinsky and V. H. Huxley and G. Guidoboni and K. Palaniappan",
  title = "Deep U-Net Regression and Hand-Crafted Feature Fusion for Accurate Blood Vessel Segmentation",
  year = 2019,
  journal = "2019 IEEE International Conference on Image Processing (ICIP)",
  publisher = "IEEE",
  pages = "1445-1449",
  month = "Aug",
  keywords = "semantic vessel segmentation, deep learning, histogram equalization, random forests, u-net",
  doi = "10.1109/ICIP.2019.8803084",
  url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=Deep+U-Net+Regression+and+Hand-Crafted+Feature+Fusion+for+Accurate+Blood+Vessel+Segmentation&btnG="
}

Y. M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, and K. Palaniappan. Deep U-Net Regression and Hand-Crafted Feature Fusion for Accurate Blood Vessel Segmentation. 2019 IEEE International Conference on Image Processing (ICIP), IEEE, pages 1445-1449, August 2019.

#305: G. Yolcu, I. Oztel, S. Kazan, C. Oz, K. Palaniappan, T. E. Lever, and F. Bunyak

Facial expression recognition for monitoring neurological disorders based on convolutional neural network

Multimedia Tools and Applications, Volume 78, pgs. 31581-31603, 2019

facial component segmentation, facial expression recognition, convolutional neural network, deep learning

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@article{2019a,
  author = "G. Yolcu and I. Oztel and S. Kazan and C. Oz and K. Palaniappan and T. E. Lever and F. Bunyak",
  title = "Facial expression recognition for monitoring neurological disorders based on convolutional neural network",
  year = 2019,
  journal = "Multimedia Tools and Applications",
  publisher = "Springer",
  volume = 78,
  number = 22,
  pages = "31581-31603",
  month = "Jul",
  keywords = "facial component segmentation, facial expression recognition, convolutional neural network, deep learning",
  doi = "10.1007/s11042-019-07959-6",
  url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=Facial+expression+recognition+for+monitoring+neurological+disorders+based+on+convolutional+neural+network&btnG="
}

G. Yolcu, I. Oztel, S. Kazan, C. Oz, K. Palaniappan, T. E. Lever, and F. Bunyak. Facial expression recognition for monitoring neurological disorders based on convolutional neural network. Multimedia Tools and Applications, Springer, volume 78, issue 22, pages 31581-31603, July 2019.

#301: K. Palaniappan, F. Bunyak, and S. S. Chaurasia

Image analysis for ophthalmology: segmentation and quantification of retinal vascular systems

Ocular Fluid Dynamics, Springer, pgs. 543--580, 2019

Bibtex, PlainText, Google Scholar

@incollection{Palaniappan:Book2019,
  author = "K. Palaniappan and F. Bunyak and S. S. Chaurasia",
  title = "Image analysis for ophthalmology: segmentation and quantification of retinal vascular systems",
  year = 2019,
  booktitle = "Ocular Fluid Dynamics",
  publisher = "Springer",
  pages = "543--580"
}

#296: L. Wen, P. Zhu, D. Du, X. Bian, H. Ling, Q. Hu, J. Zheng, T. Peng, X. Wang, Y. Zhang, L. Bo, H. Shi, R. Zhu, A. Jadhav, B. Dong, B. Lall, C. Liu, C. Zhang, D. Wang, F. Ni, F. Bunyak, G. Wang, G. Liu, G. Seetharaman, G. Li, H. Ardo, H. Zhang, H. Yu, H. Lu, J.-N. Hwang, J. Mu, J. Hu, K. Palaniappan, L. Chen, L. Ding, M. Lauer, M. Nilsson, N. M. Al-Shakarji, P. Mukherjee, Q. Huang, R. Laganiere, S. Chen, S. Pan, V. Kaushik, W. Shi, W. Tian, W. Li, X. Chen, X. Zhang, Y. Zhang, Y. Zhao, Y. Wang, Y. Song, Y. Yao, Z. Chen, Z. Xu, Z. Xiao, and Z. Tong

VisDrone-MOT2019: The vision meets drone multiple object tracking challenge results

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pgs. 189-198, 2019

drone-based multiple object tracking, drone performance evaluation, multi-object tracking

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Noor2019:Visdrone_report,
  author = "L. Wen and P. Zhu and D. Du and X. Bian and H. Ling and Q. Hu and J. Zheng and T. Peng and X. Wang and Y. Zhang and L. Bo and H. Shi and R. Zhu and A. Jadhav and B. Dong and B. Lall and C. Liu and C. Zhang and D. Wang and F. Ni and F. Bunyak and G. Wang and G. Liu and G. Seetharaman and G. Li and H. Ardo and H. Zhang and H. Yu and H. Lu and J.-N. Hwang and J. Mu and J. Hu and K. Palaniappan and L. Chen and L. Ding and M. Lauer and M. Nilsson and N. M. Al-Shakarji and P. Mukherjee and Q. Huang and R. Laganiere and S. Chen and S. Pan and V. Kaushik and W. Shi and W. Tian and W. Li and X. Chen and X. Zhang and Y. Zhang and Y. Zhao and Y. Wang and Y. Song and Y. Yao and Z. Chen and Z. Xu and Z. Xiao and Z. Tong",
  title = "VisDrone-MOT2019: The vision meets drone multiple object tracking challenge results",
  year = 2019,
  booktitle = "Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)",
  pages = "189-198",
  keywords = "drone-based multiple object tracking, drone performance evaluation, multi-object tracking",
  doi = "10.1109/ICCVW.2019.00028",
  url = "https://openaccess.thecvf.com/content_ICCVW_2019/html/VISDrone/Wen_VisDrone-MOT2019_The_Vision_Meets_Drone_Multiple_Object_Tracking_Challenge_Results_ICCVW_2019_paper.html"
}

L. Wen, P. Zhu, D. Du, X. Bian, H. Ling, Q. Hu, J. Zheng, T. Peng, X. Wang, Y. Zhang, L. Bo, H. Shi, R. Zhu, A. Jadhav, B. Dong, B. Lall, C. Liu, C. Zhang, D. Wang, F. Ni, F. Bunyak, G. Wang, G. Liu, G. Seetharaman, G. Li, H. Ardo, H. Zhang, H. Yu, H. Lu, J.-N. Hwang, J. Mu, J. Hu, K. Palaniappan, L. Chen, L. Ding, M. Lauer, M. Nilsson, N. M. Al-Shakarji, P. Mukherjee, Q. Huang, R. Laganiere, S. Chen, S. Pan, V. Kaushik, W. Shi, W. Tian, W. Li, X. Chen, X. Zhang, Y. Zhang, Y. Zhao, Y. Wang, Y. Song, Y. Yao, Z. Chen, Z. Xu, Z. Xiao, and Z. Tong. VisDrone-MOT2019: The vision meets drone multiple object tracking challenge results. Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pages 189-198, 2019.

#295: N. Al-Shakarji, F. Bunyak, H. Aliakbarpour, G. Seetharaman, and K. Palaniappan

Performance evaluation of semantic video compression using multi-cue object detection

IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1-8, 2019

deep learning, object detection, data compression, video surveillance, image motion analysis, saliency

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Noor2019:AIPR_Video_compression,
  author = "N. Al-Shakarji and F. Bunyak and H. Aliakbarpour and G. Seetharaman and K. Palaniappan",
  title = "Performance evaluation of semantic video compression using multi-cue object detection",
  year = 2019,
  booktitle = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  pages = "1-8",
  keywords = "deep learning, object detection, data compression, video surveillance, image motion analysis, saliency",
  doi = "10.1109/AIPR47015.2019.9174601",
  url = "https://ieeexplore.ieee.org/abstract/document/9174601"
}

N. Al-Shakarji, F. Bunyak, H. Aliakbarpour, G. Seetharaman, and K. Palaniappan. Performance evaluation of semantic video compression using multi-cue object detection. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1-8, 2019.

#294: R. Aktar, K. Palaniappan, and J. Uhlmann

A method for online interpolation of packet-loss blocks in streaming video

IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1-7, 2019

packet-loss, streaming video, interpolation

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{9174590,
  author = "R. Aktar and K. Palaniappan and J. Uhlmann",
  title = "A method for online interpolation of packet-loss blocks in streaming video",
  year = 2019,
  journal = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  pages = "1-7",
  keywords = "packet-loss, streaming video, interpolation",
  doi = "10.1109/AIPR47015.2019.9174590",
  url = "https://ieeexplore.ieee.org/abstract/document/9174590"
}

R. Aktar, K. Palaniappan, and J. Uhlmann. A method for online interpolation of packet-loss blocks in streaming video. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1-7, 2019.

#304: N. Al-Shakarji, F. Bunyak, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan

Multi-cue vehicle detection for semantic video compression in georegistered aerial videos

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pgs. 56-65, 2019

deep learning, object detection, data compression, video surveillance, image motion analysis

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{Noor2019:CVPRW_Multi_cue,
  author = "N. Al-Shakarji and F. Bunyak and H. AliAkbarpour and G. Seetharaman and K. Palaniappan",
  title = "Multi-cue vehicle detection for semantic video compression in georegistered aerial videos",
  year = 2019,
  journal = "Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
  pages = "56-65",
  keywords = "deep learning, object detection, data compression, video surveillance, image motion analysis",
  url = "https://openaccess.thecvf.com/content_CVPRW_2019/html/DOAI/Al-Shakarji_Multi-Cue_Vehicle_Detection_for_Semantic_Video_Compression_in_Georegistered_Aerial_CVPRW_2019_paper.html"
}

N. Al-Shakarji, F. Bunyak, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan. Multi-cue vehicle detection for semantic video compression in georegistered aerial videos. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 56-65, 2019.

#283: M. Poostchi, I. Ersoy, K. McMenamin, E. Gordon, N. Palaniappan, S. Pierce, R. J. Maude, A. Bansal, P. Srinivasan, L. Miller, K. Palaniappan, G. Thoma, and S. Jaeger

Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy

Journal of Medical Imaging, Volume 5, pgs. 1-14, 2018

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@article{2018a,
  author = "M. Poostchi and I. Ersoy and K. McMenamin and E. Gordon and N. Palaniappan and S. Pierce and R. J. Maude and A. Bansal and P. Srinivasan and L. Miller and K. Palaniappan and G. Thoma and S. Jaeger",
  title = "Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy",
  year = 2018,
  journal = "Journal of Medical Imaging",
  publisher = "International Society for Optics and Photonics",
  volume = 5,
  number = 4,
  pages = "1-14",
  month = "Dec",
  doi = "10.1117/1.JMI.5.4.044506",
  url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=Malaria+parasite+detection+and+cell+counting+for+human+and+mouse+using+thin+blood+smear+microscopy&btnG="
}

M. Poostchi, I. Ersoy, K. McMenamin, E. Gordon, N. Palaniappan, S. Pierce, R. J. Maude, A. Bansal, P. Srinivasan, L. Miller, K. Palaniappan, G. Thoma, and S. Jaeger. Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy. Journal of Medical Imaging, International Society for Optics and Photonics, volume 5, issue 4, pages 1-14, December 2018.

#279: Y. M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, and K. Palaniappan

Patch-Based Semantic Segmentation for Detecting Arterioles and Venules in Epifluorescence Imagery

IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1-5, 2018

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{2018a,
  author = "Y. M. Kassim and O. V. Glinskii and V. V. Glinsky and V. H. Huxley and K. Palaniappan",
  title = "Patch-Based Semantic Segmentation for Detecting Arterioles and Venules in Epifluorescence Imagery",
  year = 2018,
  journal = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  publisher = "IEEE",
  pages = "1-5",
  month = "Oct",
  doi = "10.1109/AIPR.2018.8707387",
  url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=Patch-Based+Semantic+Segmentation+for+Detecting+Arterioles+and+Venules+in+Epifluorescence+Imagery&btnG="
}

Y. M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, and K. Palaniappan. Patch-Based Semantic Segmentation for Detecting Arterioles and Venules in Epifluorescence Imagery. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), IEEE, pages 1-5, October 2018.

#278: R. Aktar, H. Aliakbarpour, F. Bunyak, G. Seetharaman, and K. Palaniappan

Performance evaluation of feature descriptors for aerial imagery mosaicking

IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1-7, 2018

features, mosaicing, performance evaluation, aerial video

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{218a,
  author = "R. Aktar and H. Aliakbarpour and F. Bunyak and G. Seetharaman and K. Palaniappan",
  title = "Performance evaluation of feature descriptors for aerial imagery mosaicking",
  year = 2018,
  journal = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  publisher = "IEEE",
  pages = "1-7",
  month = "Oct",
  keywords = "features, mosaicing, performance evaluation, aerial video",
  doi = "10.1109/AIPR.2018.8707402",
  url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=Performance+Evaluation+of+Feature+Descriptors+for+Aerial+Imagery+Mosaicking&btnG="
}

R. Aktar, H. Aliakbarpour, F. Bunyak, G. Seetharaman, and K. Palaniappan. Performance evaluation of feature descriptors for aerial imagery mosaicking. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), IEEE, pages 1-7, October 2018.

#290: J. Patman, S. C. J. Michael, M. M. F. Lutnesky, and K. Palaniappan

BioSense: Real-Time Object Tracking for Animal Movement and Behavior Research

IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1-8, 2018

object-tracking, video analytics, animal behavior, automated data collection, tracking software

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{2018a,
  author = "J. Patman and S. C. J. Michael and M. M. F. Lutnesky and K. Palaniappan",
  title = "BioSense: Real-Time Object Tracking for Animal Movement and Behavior Research",
  year = 2018,
  journal = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  publisher = "IEEE",
  pages = "1-8",
  month = "Oct",
  keywords = "object-tracking, video analytics, animal behavior, automated data collection, tracking software",
  doi = "10.1109/AIPR.2018.8707411",
  url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=BioSense%3A+Real-Time+Object+Tracking+for+Animal+Movement+and+Behavior+Research&btnG="
}

J. Patman, S. C. J. Michael, M. M. F. Lutnesky, and K. Palaniappan. BioSense: Real-Time Object Tracking for Animal Movement and Behavior Research. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), IEEE, pages 1-8, October 2018.

#273: Y. M. Kassim, R. J. Maude, and K. Palaniappan

Sensitivity of Cross-Trained Deep CNNs for Retinal Vessel Extraction

40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pgs. 2736-2739, 2018

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{2018a,
  author = "Y. M. Kassim and R. J. Maude and K. Palaniappan",
  title = "Sensitivity of Cross-Trained Deep CNNs for Retinal Vessel Extraction",
  year = 2018,
  journal = "40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)",
  publisher = "IEEE",
  pages = "2736-2739",
  month = "Oct",
  doi = "10.1109/EMBC.2018.8512764",
  url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=Sensitivity+of+Cross-Trained+Deep+CNNs+for+Retinal+Vessel+Extraction&btnG="
}

Y. M. Kassim, R. J. Maude, and K. Palaniappan. Sensitivity of Cross-Trained Deep CNNs for Retinal Vessel Extraction. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pages 2736-2739, October 2018.

#284: Z. A. Oraibi, H. Yousif, A. Hafiane, G. Seetharaman, and K. Palaniappan

Learning Local and Deep Features for Efficient Cell Image Classification Using Random Forests

25th IEEE International Conference on Image Processing (ICIP), pgs. 2446-2450, 2018

feature extraction, random forests, local features, deep learning, image classification

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{2018a,
  author = "Z. A. Oraibi and H. Yousif and A. Hafiane and G. Seetharaman and K. Palaniappan",
  title = "Learning Local and Deep Features for Efficient Cell Image Classification Using Random Forests",
  year = 2018,
  journal = "25th IEEE International Conference on Image Processing (ICIP)",
  publisher = "IEEE",
  pages = "2446-2450",
  month = "Sep",
  keywords = "feature extraction, random forests, local features, deep learning, image classification",
  doi = "10.1109/ICIP.2018.8451287",
  url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=Learning+Local+and+Deep+Features+for+Efficient+Cell+Image+Classification+Using+Random+Forests&btnG="
}

Z. A. Oraibi, H. Yousif, A. Hafiane, G. Seetharaman, and K. Palaniappan. Learning Local and Deep Features for Efficient Cell Image Classification Using Random Forests. 25th IEEE International Conference on Image Processing (ICIP), IEEE, pages 2446-2450, September 2018.

#280: D. Chemodanov, P. Calyam, S. Valluripally, H. Trinh, J. Patman, and K. Palaniappan

On QoE-oriented Cloud Service Orchestration for Application Providers

IEEE Transactions on Services Computing, pgs. 1-14, 2018

qoe-oriented cloud service orchestration, multiconstrained path-aware possibilistic c-mean clustering, 3q interplay model, least cost-disruptive decision tree

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@article{2018a,
  author = "D. Chemodanov and P. Calyam and S. Valluripally and H. Trinh and J. Patman and K. Palaniappan",
  title = "On QoE-oriented Cloud Service Orchestration for Application Providers",
  year = 2018,
  journal = "IEEE Transactions on Services Computing",
  publisher = "IEEE",
  pages = "1-14",
  month = "Aug",
  keywords = "qoe-oriented cloud service orchestration, multiconstrained path-aware possibilistic c-mean clustering, 3q interplay model, least cost-disruptive decision tree",
  doi = "10.1109/TSC.2018.2866851",
  url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=On+QoE-oriented+Cloud+Service+Orchestration+for+Application+Providers&btnG="
}

D. Chemodanov, P. Calyam, S. Valluripally, H. Trinh, J. Patman, and K. Palaniappan. On QoE-oriented Cloud Service Orchestration for Application Providers. IEEE Transactions on Services Computing, IEEE, pages 1-14, August 2018.

#288: H. Trinh, P. Calyam, D. Chemodanov, S. Yao, Q. Lei, F. Gao, and K. Palaniappan

Energy-Aware Mobile Edge Computing and Routing for Low-Latency Visual Data Processing

IEEE Transactions on Multimedia, Volume 20, pgs. 2562 - 2577, 2018

mobile edge computing, policy-based cloud management, energy-aware edge routing, low-latency visual data processing

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

New paradigms such as Mobile Edge Computing (MEC) are becoming feasible for use in, e.g., real-time decision-making during disaster incident response to handle the data deluge occurring in the network edge. However, MEC deployments today lack flexible IoT device data handling such as handling user preferences for real-time versus energy-efficient processing. Moreover, MEC can also benefit from a policy-based edge routing to handle sustained performance levels with efficient energy consumption. In this paper, we study the potential of MEC to address application issues related to energy management on constrained IoT devices with limited power sources, while also providing low-latency processing of visual data being generated at high resolutions. Using a facial recognition application that is important in disaster incident response scenarios, we propose a novel “offload decision-making” algorithm that analyzes the tradeoffs in computing policies to offload visual data processing (i.e., to an edge cloud or a core cloud) at low-to-high workloads. This algorithm also analyzes the impact on energy consumption in the decision-making under different visual data consumption requirements (i.e., users with thick clients or thin clients). To address the processing-throughput versus energy-efficiency tradeoffs, we propose a “Sustainable Policy-based Intelligence-Driven Edge Routing” algorithm that uses machine learning within Mobile Ad hoc Networks. This algorithm is energy aware and improves the geographic routing baseline performance (i.e., minimizes impact of local minima) for throughput performance sustainability, while also enabling flexible policy specification. We evaluate our proposed algorithms by conducting experiments on a realistic edge and core cloud testbed in the GENI Cloud infrastructure, and recreate disaster scenes of tornado damages within simulations. Our empirical results show how MEC can provide flexibility to users who desire energy conservation over low latency or vice versa in the visual data processing with a facial recognition application. In addition, our simulation results show that our routing approach outperforms existing solutions under diverse user preferences, node mobility, and severe node failure conditions.

@article{2018a,
  author = "H. Trinh and P. Calyam and D. Chemodanov and S. Yao and Q. Lei and F. Gao and K. Palaniappan",
  title = "Energy-Aware Mobile Edge Computing and Routing for Low-Latency Visual Data Processing",
  year = 2018,
  journal = "IEEE Transactions on Multimedia",
  publisher = "IEEE",
  volume = 20,
  number = 10,
  pages = "2562 - 2577",
  month = "Aug",
  keywords = "mobile edge computing, policy-based cloud management, energy-aware edge routing, low-latency visual data processing",
  doi = " 10.1109/TMM.2018.2865661",
  url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=Energy-Aware+Mobile+Edge+Computing+and+Routing+for+Low-Latency+Visual+Data+Processing&btnG="
}

H. Trinh, P. Calyam, D. Chemodanov, S. Yao, Q. Lei, F. Gao, and K. Palaniappan. Energy-Aware Mobile Edge Computing and Routing for Low-Latency Visual Data Processing. IEEE Transactions on Multimedia, IEEE, volume 20, issue 10, pages 2562 - 2577, August 2018.

#289: F. Bunyak, N. Shiraishi, K. Palaniappan, T. E. Lever, L. Avivi-Arber, and K. Takahashi

Development of semi-automatic procedure for detection and tracking of fiducial markers for orofacial kinematics during natural feeding

In Engineering in Medicine and Biology Society (EMBC), pgs. 580-583, 2018

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{2018a,
  author = "F. Bunyak and N. Shiraishi and K. Palaniappan and T.  E. Lever and L. Avivi-Arber and K. Takahashi",
  title = "Development of semi-automatic procedure for detection and tracking of fiducial markers for orofacial kinematics during natural feeding",
  year = 2018,
  journal = "In Engineering in Medicine and Biology Society (EMBC)",
  pages = "580-583",
  month = "Jul",
  url = "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5787344/pdf/nihms935834.pdf"
}

F. Bunyak, N. Shiraishi, K. Palaniappan, T.  E. Lever, L. Avivi-Arber, and K. Takahashi. Development of semi-automatic procedure for detection and tracking of fiducial markers for orofacial kinematics during natural feeding. In Engineering in Medicine and Biology Society (EMBC), pages 580-583, July 2018.

#287: S. Yao, H. Yu, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan

EpiX: A 3D Measurement Tool for Heritage, Archeology and Aerial Photogrammetry

Springer Book Chapter, 2018

cultural heritage, wide-area motion imagery, annotation tool, epipolar geometry

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{2018a,
  author = "S. Yao and H. Yu and H. AliAkbarpour and G. Seetharaman and K. Palaniappan",
  title = "EpiX: A 3D Measurement Tool for Heritage, Archeology and Aerial Photogrammetry",
  year = 2018,
  journal = "Springer Book Chapter",
  month = "Jun",
  keywords = "cultural heritage, wide-area motion imagery, annotation tool, epipolar geometry"
}

S. Yao, H. Yu, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan. EpiX: A 3D Measurement Tool for Heritage, Archeology and Aerial Photogrammetry. Springer Book Chapter, June 2018.

#292: S. Yao, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan

3D patch-based multi-view stereo for high-resolution imagery

Geospatial Informatics, Motion Imagery, and Network Analytics VIII, pgs. 1-9, 2018

3d patch-based mvs, wami, 3d reconstruction, photometric consistency

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{2018a,
  author = "S. Yao and H. AliAkbarpour and G. Seetharaman and K. Palaniappan",
  title = "3D patch-based multi-view stereo for high-resolution imagery",
  year = 2018,
  journal = "Geospatial Informatics, Motion Imagery, and Network Analytics VIII",
  pages = "1-9",
  month = "Apr",
  keywords = "3d patch-based mvs, wami, 3d reconstruction, photometric consistency",
  doi = "10.1117/12.2309806",
  url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=3D+patch-based+multi-view+stereo+for+high-resolution+imagery&btnG="
}

S. Yao, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan. 3D patch-based multi-view stereo for high-resolution imagery. Geospatial Informatics, Motion Imagery, and Network Analytics VIII, pages 1-9, April 2018.

#277: J. Patman, M. Alfarhood, S. Islam, M. Lemus, P. Calyam, and K. Palaniappan

Predictive Analytics for Fog Computing using Machine Learning and GENI

IEEE INFOCOM International Workshop on Computer and Networking Experimental Research Using Testbeds (CNERT), 2018

fog computing, machine learning, predictive analytics, software-defined networking, experimental testbed

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{2018a,
  author = "J. Patman and M. Alfarhood and S. Islam and M. Lemus and P. Calyam and K. Palaniappan",
  title = "Predictive Analytics for Fog Computing using Machine Learning and GENI",
  year = 2018,
  journal = "IEEE INFOCOM International Workshop on Computer and Networking Experimental Research Using Testbeds (CNERT)",
  month = "Apr",
  keywords = "fog computing, machine learning, predictive analytics, software-defined networking, experimental testbed",
  url = "https://www.researchgate.net/profile/Jon_Patman/publication/325324436_Predictive_Analytics_for_Fog_Computing_using_Machine_Learning_and_GENI/links/5b05d9efaca2725783d89ba5/Predictive-Analytics-for-Fog"
}

J. Patman, M. Alfarhood, S. Islam, M. Lemus, P. Calyam, and K. Palaniappan. Predictive Analytics for Fog Computing using Machine Learning and GENI. IEEE INFOCOM International Workshop on Computer and Networking Experimental Research Using Testbeds (CNERT), April 2018.

#276: E. Teters, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan

Real-time geoprojection and stabilization on airborne GPU-enabled embedded systems

SPIE Geospatial Informatics, and Motion Imagery Analytics VIII, Volume 10645, pgs. 106450H, 2018

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{2018a,
  author = "E. Teters and H. AliAkbarpour and G. Seetharaman and K. Palaniappan",
  title = "Real-time geoprojection and stabilization on airborne GPU-enabled embedded systems",
  year = 2018,
  journal = "SPIE Geospatial Informatics, and Motion Imagery Analytics VIII",
  volume = 10645,
  pages = "106450H",
  month = "Apr",
  doi = " https://doi.org/10.1117/12.2309796",
  url = "https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10645/106450H/Real-time-geoprojection-and-stabilization-on-airborne-GPU-enabled-embedded/10.1117/12.2309796.full?SSO=1"
}

E. Teters, H. AliAkbarpour, G. Seetharaman, and K. Palaniappan. Real-time geoprojection and stabilization on airborne GPU-enabled embedded systems. SPIE Geospatial Informatics, and Motion Imagery Analytics VIII, volume 10645, pages 106450H, April 2018.

#285: R. Aktar, H. AliAkbarpour, F. Bunyak, T. Kazic, G. Seetharaman, and K. Palaniappan

Geospatial content summarization of UAV aerial imagery using mosaicking

International Society for Optics and Photonics, Volume 10645, pgs. 106450I, 2018

aerial imagery, mosaicking, registration, summarization, feature based registration.

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{2018a,
  author = "R. Aktar and H. AliAkbarpour and F. Bunyak and T. Kazic and G. Seetharaman and K. Palaniappan",
  title = "Geospatial content summarization of UAV aerial imagery using mosaicking",
  year = 2018,
  journal = "International Society for Optics and Photonics",
  booktitle = "Geospatial Informatics, Motion Imagery, and Network Analytics VIII",
  volume = 10645,
  pages = "106450I",
  month = "Apr",
  keywords = "aerial imagery, mosaicking, registration, summarization, feature based registration.",
  url = "https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10645/106450I/Geospatial-content-summarization-of-UAV-aerial-imagery-using-mosaicking/10.1117/12.2309417.full?SSO=1"
}

R. Aktar, H. AliAkbarpour, F. Bunyak, T. Kazic, G. Seetharaman, and K. Palaniappan. Geospatial content summarization of UAV aerial imagery using mosaicking. International Society for Optics and Photonics, Geospatial Informatics, Motion Imagery, and Network Analytics VIII, volume 10645, pages 106450I, April 2018.

#274: A. Bouix, N. Al-Shakarji, K. Gao, F. Bunyak, A. Chazot, A. Hafiane, and K. Palaniappan

Robust target tracking using adaptive color feature and likelihood fusion

International Society for Optics and Photonics, Volume 10645, pgs. 106450L, 2018

visual object tracking, staple, correlation filter, color name, fusion

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Bouix2018:AIPR_iSTAPLE,
  author = "A. Bouix and N. Al-Shakarji and K. Gao and F. Bunyak and A. Chazot and A. Hafiane and K. Palaniappan",
  title = "Robust target tracking using adaptive color feature and likelihood fusion",
  year = 2018,
  journal = "International Society for Optics and Photonics",
  booktitle = "Geospatial Informatics, Motion Imagery, and Network Analytics VIII",
  volume = 10645,
  pages = "106450L",
  month = "Apr",
  keywords = "visual object tracking, staple, correlation filter, color name, fusion",
  doi = "10.1117/12.2309807",
  url = "https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10645/106450L/Robust-target-tracking-using-adaptive-color-feature-and-likelihood-fusion/10.1117/12.2309807.full"
}

A. Bouix, N. Al-Shakarji, K. Gao, F. Bunyak, A. Chazot, A. Hafiane, and K. Palaniappan. Robust target tracking using adaptive color feature and likelihood fusion. International Society for Optics and Photonics, Geospatial Informatics, Motion Imagery, and Network Analytics VIII, volume 10645, pages 106450L, April 2018.

#286: K. Gao, H. AliAkbarpour, K. Palaniappan, and G. Seetharaman

Evaluation of feature matching in aerial imagery for Structure-from Motion and bundle adjustment

International Society for Optics and Photonics, Volume 10645, pgs. 106450J, 2018

feature matching, evaluation, structure-from-motion, 3d reconstruction

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{KeGao2018:SPIE_features-ba4s,
  author = "K. Gao and H. AliAkbarpour and K. Palaniappan and G. Seetharaman",
  title = "Evaluation of feature matching in aerial imagery for Structure-from Motion and bundle adjustment",
  year = 2018,
  journal = "International Society for Optics and Photonics",
  booktitle = "Geospatial Informatics, Motion Imagery, and Network Analytics VIII",
  volume = 10645,
  pages = "106450J",
  month = "Apr",
  keywords = "feature matching, evaluation, structure-from-motion, 3d reconstruction",
  doi = "10.1117/12.2309805",
  url = "https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10645/106450J/Evaluation-of-feature-matching-in-aerial-imagery-for-structure-from/10.1117/12.2309805.full?SSO=1"
}

K. Gao, H. AliAkbarpour, K. Palaniappan, and G. Seetharaman. Evaluation of feature matching in aerial imagery for Structure-from Motion and bundle adjustment. International Society for Optics and Photonics, Geospatial Informatics, Motion Imagery, and Network Analytics VIII, volume 10645, pages 106450J, April 2018.

#281: J. Peng, A. J. Aved, G. Seetharaman, and K. Palaniappan

Multiview boosting with information propagation for classification

IEEE Transactions on Neural Networks and Learning Systems, Volume 29, pgs. 657-669, 2018

Bibtex, PlainText, URL, Google Scholar

@inproceedings{2018a,
  author = "J. Peng and A. J. Aved and G. Seetharaman and K. Palaniappan",
  title = "Multiview boosting with information propagation for classification",
  year = 2018,
  journal = "IEEE Transactions on Neural Networks and Learning Systems",
  volume = 29,
  number = 3,
  pages = "657-669",
  month = "Mar",
  url = "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7805338"
}

#266: P. Zhu, L. Wen, D. Du, X. Bian, H. Ling, Q. Hu, H. Wu, Q. Nie, H. Cheng, C. Liu, X. Liu, W. Ma, L. Wang, A. Schumann, D. Wang, D. Ortego, E. Luna, E. Michail, E. Bochinski, F. Ni, F. Bunyak, G. Zhang, G. Seetharaman, G. Li, H. Yu, I. Kompatsiaris, J. Zhao, J. Gao, J. M. Martinez, J. C. S. Miguel, K. Palaniappan, K. Avgerinakis, L. Sommer, M. Lauer, M. Liu, N. M. Al-Shakarji, O. Acatay, P. Giannakeris, Q. Zhao, Q. Ma, Q. Huang, S. Vrochidis, T. Sikora, T. Senst, W. Song, W. Tian, W. Zhang, Y. Zhao, Y. Bai, Y. Wu, Y. Wang, Y. Li, Z. Pi, and Z. Ma

VisDrone-VDT2018: The vision meets drone video detection and tracking challenge results

Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018

drone-based multiple object tracking, drone performance evaluation, multi-object tracking

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{Noor2018:ECCVW_Visdrone_Report,
  author = "P. Zhu and L. Wen and D. Du and X. Bian and H. Ling and Q. Hu and H. Wu and Q. Nie and H. Cheng and C. Liu and X. Liu and W. Ma and L. Wang and A. Schumann and D. Wang and D. Ortego and E. Luna and E. Michail and E. Bochinski and F. Ni and F. Bunyak and G. Zhang and G. Seetharaman and G. Li and H. Yu and I. Kompatsiaris and J. Zhao and J. Gao and J. M. Martinez and J. C. S. Miguel and K. Palaniappan and K. Avgerinakis and L. Sommer and M. Lauer and M. Liu and N. M. Al-Shakarji and O. Acatay and P. Giannakeris and Q. Zhao and Q. Ma and Q. Huang and S. Vrochidis and T. Sikora and T. Senst and W. Song and W. Tian and W. Zhang and Y. Zhao and Y. Bai and Y. Wu and Y. Wang and Y. Li and Z. Pi and Z. Ma",
  title = "VisDrone-VDT2018: The vision meets drone video detection and tracking challenge results",
  year = 2018,
  booktitle = "Proceedings of the European Conference on Computer Vision (ECCV) Workshops",
  keywords = "drone-based multiple object tracking, drone performance evaluation, multi-object tracking",
  url = "https://openaccess.thecvf.com/content_eccv_2018_workshops/w27/html/Zhu_VisDrone-VDT2018_The_Vision_Meets_Drone_Video_Detection_and_Tracking_Challenge_ECCVW_2018_paper.html"
}

P. Zhu, L. Wen, D. Du, X. Bian, H. Ling, Q. Hu, H. Wu, Q. Nie, H. Cheng, C. Liu, X. Liu, W. Ma, L. Wang, A. Schumann, D. Wang, D. Ortego, E. Luna, E. Michail, E. Bochinski, F. Ni, F. Bunyak, G. Zhang, G. Seetharaman, G. Li, H. Yu, I. Kompatsiaris, J. Zhao, J. Gao, J. M. Martinez, J. C. S. Miguel, K. Palaniappan, K. Avgerinakis, L. Sommer, M. Lauer, M. Liu, N. M. Al-Shakarji, O. Acatay, P. Giannakeris, Q. Zhao, Q. Ma, Q. Huang, S. Vrochidis, T. Sikora, T. Senst, W. Song, W. Tian, W. Zhang, Y. Zhao, Y. Bai, Y. Wu, Y. Wang, Y. Li, Z. Pi, and Z. Ma. VisDrone-VDT2018: The vision meets drone video detection and tracking challenge results. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018.

#265: S. Lyu, M.-C. Chang, D. Du, W. Li, Y. Wei, M. D. Coco, P. Carcagnì, A. Schumann, B. Munjal, D.-Q. Dang, D.-H. Choi, E. Bochinski, F. Galasso, F. Bunyak, G. Seetharaman, J.-W. Baek, J. T. Lee, K. Palaniappan, K.-T. Lim, K. Moon, K.-J. Kim, L. Sommer, M. Brandlmaier, M.-S. Kang, M. Jeon, N. M. Al-Shakarji, O. Acatay, P.-K. Kim, S. Amin, T. Sikora, T. Dinh, T. Senst, V.-G. Che, Y.-C. Lim, Y.-m. Song, and Y.-S. Chung

UA-DETRAC 2018: Report of AVSS2018 & IWT4S challenge on advanced traffic monitoring

IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pgs. 1-6, 2018

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Noor2018:UA-Detrac_report,
  author = "S. Lyu and M.-C. Chang and D. Du and W. Li and Y. Wei and M. D. Coco and P. Carcagnì and A. Schumann and B. Munjal and D.-Q. Dang and D.-H. Choi and E. Bochinski and F. Galasso and F. Bunyak and G. Seetharaman and J.-W. Baek and J. T. Lee and K. Palaniappan and K.-T. Lim and K. Moon and K.-J. Kim and L. Sommer and M. Brandlmaier and M.-S. Kang and M. Jeon and N. M. Al-Shakarji and O. Acatay and P.-K. Kim and S. Amin and T. Sikora and T. Dinh and T. Senst and V.-G. Che and Y.-C. Lim and Y.-m. Song and Y.-S. Chung",
  title = "UA-DETRAC 2018: Report of AVSS2018 & IWT4S challenge on advanced traffic monitoring",
  year = 2018,
  booktitle = "IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)",
  pages = "1-6",
  doi = "10.1109/AVSS.2018.8639089",
  url = "https://ieeexplore.ieee.org/abstract/document/8639089"
}

S. Lyu, M.-C. Chang, D. Du, W. Li, Y. Wei, M. D. Coco, P. Carcagnì, A. Schumann, B. Munjal, D.-Q. Dang, D.-H. Choi, E. Bochinski, F. Galasso, F. Bunyak, G. Seetharaman, J.-W. Baek, J. T. Lee, K. Palaniappan, K.-T. Lim, K. Moon, K.-J. Kim, L. Sommer, M. Brandlmaier, M.-S. Kang, M. Jeon, N. M. Al-Shakarji, O. Acatay, P.-K. Kim, S. Amin, T. Sikora, T. Dinh, T. Senst, V.-G. Che, Y.-C. Lim, Y.-m. Song, and Y.-S. Chung. UA-DETRAC 2018: Report of AVSS2018 & IWT4S challenge on advanced traffic monitoring. IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pages 1-6, 2018.

#282: N. Al-Shakarji, F. Bunyak, G. Seetharaman, and K. Palaniappan

Multi-object tracking cascade with multi-step data association and occlusion handling

IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pgs. 1-6, 2018

multi-object tracking, tracking, occlusion handling, association

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Noor2019:MOT_Cascade_Mulit_step,
  author = "N. Al-Shakarji and F. Bunyak and G. Seetharaman and K. Palaniappan",
  title = "Multi-object tracking cascade with multi-step data association and occlusion handling",
  year = 2018,
  journal = "IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)",
  publisher = "IEEE",
  pages = "1-6",
  keywords = "multi-object tracking, tracking, occlusion handling, association",
  doi = "10.1109/AVSS.2018.8639321",
  url = "https://ieeexplore.ieee.org/abstract/document/8639321"
}

N. Al-Shakarji, F. Bunyak, G. Seetharaman, and K. Palaniappan. Multi-object tracking cascade with multi-step data association and occlusion handling. IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pages 1-6, 2018.

#275: N. Al-Shakarji, F. Bunyak, G. Seetharaman, and K. Palaniappan

Robust multi-object tracking for wide area motion imagery

IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1-5, 2018

tracking, wide area motion imagery, multi-object tracking, tracking-by-detection, data association

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Noor2018:AIPR_MOT_WAMI,
  author = "N. Al-Shakarji and F. Bunyak and G. Seetharaman and K. Palaniappan",
  title = "Robust multi-object tracking for wide area motion imagery",
  year = 2018,
  journal = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  pages = "1-5",
  keywords = "tracking, wide area motion imagery, multi-object tracking, tracking-by-detection, data association",
  doi = "10.1109/AIPR.2018.8707377",
  url = "https://ieeexplore.ieee.org/abstract/document/8707377"
}

N. Al-Shakarji, F. Bunyak, G. Seetharaman, and K. Palaniappan. Robust multi-object tracking for wide area motion imagery. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1-5, 2018.

#291: N. M. Al-Shakarji, F. Bunyak, G. Seetharaman, and K. Palaniappan

A hybrid local and global multi-object tracking with semantic spatial and appearance modules

International Society for Optics and Photonics , Volume 10645, pgs. 106450B, 2018

multi-object tracking, tracking-by-detection, global data association, local data association, appearance modules

Abstract, Bibtex, PlainText, URL, Google Scholar

@inproceedings{2018a,
  author = "N. M. Al-Shakarji and F. Bunyak and G. Seetharaman and K. Palaniappan",
  title = "A hybrid local and global multi-object tracking with semantic spatial and appearance modules",
  year = 2018,
  journal = "International Society for Optics and Photonics  ",
  volume = 10645,
  pages = "106450B",
  keywords = "multi-object tracking, tracking-by-detection, global data association, local data association, appearance modules",
  url = "https://spiedigitallibrary.org/conference-proceedings-of-spie/10645/106450B/A-hybrid-local-and-global-multi-object-tracking-with-semantic/10.1117/12.2309315.short"
}

N. M. Al-Shakarji, F. Bunyak, G. Seetharaman, and K. Palaniappan. A hybrid local and global multi-object tracking with semantic spatial and appearance modules. International Society for Optics and Photonics  , volume 10645, pages 106450B, 2018.

#293: Y. M. Kassim, N. Al-Shakarji, E. Asante, A. Chandrasekhar, and K. Palaniappan

Dissecting branchiomotor neuron circuits in zebrafish - Toward high-throughput automated analysis of jaw movements

IEEE International Symposium on Biomedical Imaging (ISBI), pgs. 943-947, 2018

zebrafish, branchiomotor circuit, behavior, video microscopy, optical flow, background subtraction, motion map

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Yasmin2018:ISBI_ZebraFish,
  author = "Y. M. Kassim and N. Al-Shakarji and E. Asante and A. Chandrasekhar and K. Palaniappan",
  title = " Dissecting branchiomotor neuron circuits in zebrafish - Toward high-throughput automated analysis of jaw movements",
  year = 2018,
  journal = "IEEE International Symposium on Biomedical Imaging (ISBI)",
  pages = "943-947",
  keywords = "zebrafish, branchiomotor circuit, behavior, video microscopy, optical flow, background subtraction, motion map",
  doi = "10.1109/ISBI.2018.8363726",
  url = "https://ieeexplore.ieee.org/abstract/document/8363726/"
}

Y. M. Kassim, N. Al-Shakarji, E. Asante, A. Chandrasekhar, and K. Palaniappan.  Dissecting branchiomotor neuron circuits in zebrafish - Toward high-throughput automated analysis of jaw movements. IEEE International Symposium on Biomedical Imaging (ISBI), pages 943-947, 2018.

#248: X. Yang, G. Dong, K. Palaniappan, G. Mi, and T. Baskin

Temperature-compensated cell production rate and elongation zone length in the root of Arabidopsis thaliana

Plant, Cell & Environment, Volume 40, pgs. 264-276, 2017

rootflow, stripflow, biology

Abstract, Bibtex, PlainText, Software, PDF, URL, DOI, Google Scholar

@article{Yang:plant-Stripflow-2016,
  author = "X. Yang and G. Dong and K. Palaniappan and G. Mi and T. Baskin",
  title = "Temperature-compensated cell production rate and elongation zone length in the root of Arabidopsis thaliana",
  year = 2017,
  journal = "Plant, Cell & Environment",
  volume = 40,
  pages = "264-276",
  month = "Dec",
  keywords = "rootflow, stripflow, biology",
  doi = "10.1111/pce.12855",
  url = "https://onlinelibrary.wiley.com/doi/full/10.1111/pce.12855"
}

X. Yang, G. Dong, K. Palaniappan, G. Mi, and T. Baskin. Temperature-compensated cell production rate and elongation zone length in the root of Arabidopsis thaliana. Plant, Cell & Environment, volume 40, pages 264-276, December 2017.

#249: X. Yang, G. Dong, K. Palaniappan, G. Mi, and T. I. Baskin

Temperature-compensated cell production rate and elongation zone length in the root of Arabidopsis thaliana

Plant, Cell & Environment, 2017

rootflow, stripflow, biology

Abstract, Bibtex, PlainText, Software, PDF, DOI, Google Scholar

@article{Yang:plant-Stripflow-2016,
  author = "X. Yang and G. Dong and K. Palaniappan and G. Mi and T. I. Baskin",
  title = "Temperature-compensated cell production rate and elongation zone length in the root of Arabidopsis thaliana",
  year = 2017,
  journal = "Plant, Cell & Environment",
  month = "Dec",
  keywords = "rootflow, stripflow, biology",
  doi = "10.1111/pce.12855"
}

X. Yang, G. Dong, K. Palaniappan, G. Mi, and T. I. Baskin. Temperature-compensated cell production rate and elongation zone length in the root of Arabidopsis thaliana. Plant, Cell & Environment, December 2017.

#259: Y. M. Kassim and K. Palaniappan

Extracting Retinal Vascular Networks Using Deep Learning Architecture

IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017

deep learning, vessel segmentation, fundoscopy images, patches, convolution neural network

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{2017a,
  author = "Y. M. Kassim and K. Palaniappan",
  title = "Extracting Retinal Vascular Networks Using Deep Learning Architecture",
  year = 2017,
  journal = "IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
  month = "Nov",
  keywords = "deep learning, vessel segmentation, fundoscopy images, patches, convolution neural network",
  url = "https://ieeexplore.ieee.org/document/8217823/"
}

Y. M. Kassim and K. Palaniappan. Extracting Retinal Vascular Networks Using Deep Learning Architecture. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), November 2017.

#261: G. Yolcu, I. Oztel, S. Kazan, C. Oz, K. Palaniappan, T. Lever, and F. Bunyak

Deep learning-based facial expression recognition for monitoring neurological disorders

EEE International Conference on Bioinformatics and Biomedicine (BIBM) , pgs. 1652-1657, 2017

convolutional neural networks, deep learning, facial component segmentation, facial expression recognition

Abstract, Bibtex, PlainText, URL, Google Scholar

@inproceedings{2018a,
  author = "G. Yolcu and I. Oztel and S. Kazan and C. Oz and K. Palaniappan and T. Lever and F. Bunyak",
  title = "Deep learning-based facial expression recognition for monitoring neurological disorders",
  year = 2017,
  journal = "EEE International Conference on Bioinformatics and Biomedicine (BIBM) ",
  pages = "1652-1657",
  month = "Nov",
  keywords = "convolutional neural networks, deep learning, facial component segmentation, facial expression recognition",
  url = "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8217907"
}

G. Yolcu, I. Oztel, S. Kazan, C. Oz, K. Palaniappan, T. Lever, and F. Bunyak. Deep learning-based facial expression recognition for monitoring neurological disorders. EEE International Conference on Bioinformatics and Biomedicine (BIBM) , pages 1652-1657, November 2017.

#255: K. Gao, T. White, K. Palaniappan, M. Warmund, and F. Bunyak

MUSeed: A mobile image analysis application for plant seed morphometry

IEEE International Conference on Image Processing, pgs. 2826-2830, 2017

plant seed morphometry, image analysis, mobile application

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{KeGao2017:ICIP_MUSeed,
  author = "K. Gao and T. White and K. Palaniappan and M. Warmund and F. Bunyak",
  title = "MUSeed: A mobile image analysis application for plant seed morphometry",
  year = 2017,
  journal = "IEEE International Conference on Image Processing",
  pages = "2826-2830",
  month = "Sep",
  keywords = "plant seed morphometry, image analysis, mobile application",
  doi = "10.1109/ICIP.2017.8296798",
  url = "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8296798"
}

K. Gao, T. White, K. Palaniappan, M. Warmund, and F. Bunyak. MUSeed: A mobile image analysis application for plant seed morphometry. IEEE International Conference on Image Processing, pages 2826-2830, September 2017.

#258: Z. Al-Milajia, I. Ersoy, A. Hafiane, K. Palaniappan, and F.Bunyak

Integrating segmentation with deep learning for enhanced classification of epithelial and stromal tissues in H&E images

Pattern Recognition Letters, 2017

Bibtex, PlainText, PDF, URL, DOI, Google Scholar

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  author = "Z. Al-Milajia and I. Ersoy and A. Hafiane and K. Palaniappan and F.Bunyak",
  title = "Integrating segmentation with deep learning for enhanced classification of epithelial and stromal tissues in H&E images",
  year = 2017,
  journal = "Pattern Recognition Letters",
  month = "Sep",
  doi = "10.1016/j.patrec.2017.09.015",
  url = "https://reader.elsevier.com/reader/sd/DCEFD43DFFC270D9ABABF80CCADCB9947FF58D0788EC39D68C850BA858FC72609EFED312575ECE955CF85CA37CBDA90A"
}

#257: Y. M. Kassim, V. B. S. Prasath, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, and K. Palaniappan

Microvasculature segmentation of arterioles using deep CNN

IEEE International Conference on Image Processing (ICIP), 2017

deep learning, segmentation, vasculature, biomedical

Abstract, Bibtex, PlainText, URL, Google Scholar

@inproceedings{Yasmin:Deep-Vessel-ICIP2017,
  author = "Y. M. Kassim and V. B. S. Prasath and O. V. Glinskii and V. V. Glinsky and V. H. Huxley and K. Palaniappan",
  title = "Microvasculature segmentation of arterioles using deep CNN",
  year = 2017,
  journal = "IEEE International Conference on Image Processing (ICIP)",
  month = "Sep",
  keywords = "deep learning, segmentation, vasculature, biomedical",
  url = "http://2017.ieeeicip.org/"
}

Y. M. Kassim, V. B. S. Prasath, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, and K. Palaniappan. Microvasculature segmentation of arterioles using deep CNN. IEEE International Conference on Image Processing (ICIP), September 2017.

#250: M. Poostchi, K. Palaniappan, and G. Seetharaman

Spatial pyramid context-aware moving vehicle detection and tracking in urban aerial imagery

IEEE AVSS International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S), pgs. 1-6, 2017

Abstract, Bibtex, PlainText, URL, Google Scholar

@inproceedings{2017a,
  author = "M. Poostchi and K. Palaniappan and G. Seetharaman",
  title = "Spatial pyramid context-aware moving vehicle detection and tracking in urban aerial imagery",
  year = 2017,
  journal = "IEEE AVSS International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S)",
  pages = "1-6",
  month = "Aug",
  url = "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8078504"
}

M. Poostchi, K. Palaniappan, and G. Seetharaman. Spatial pyramid context-aware moving vehicle detection and tracking in urban aerial imagery. IEEE AVSS International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S), pages 1-6, August 2017.

#253: H. Aliakbarpour, K. Palaniappan, and G. Seetharaman

Parallax tolerant aerial image georegistration and efficient camera pose refinement - Without piecewise homographies

IEEE Transactions on Geoscience and Remote Sensing, Volume 55, pgs. 4618-4637, 2017

cameras, image registration, remote sensing, 3d reconstruction, mavmap, parallax-tolerant aerial image georegistration, pix4d, sfm method, structure from motion, visualsfm, camera metadata measurement, camera pose refinement, camera-induce

Abstract, Bibtex, PlainText, URL, DOI, Google Scholar

@inproceedings{2017a,
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  title = "Parallax tolerant aerial image georegistration and efficient camera pose refinement - Without piecewise homographies",
  year = 2017,
  journal = "IEEE Transactions on Geoscience and Remote Sensing",
  volume = 55,
  number = 8,
  pages = "4618-4637",
  month = "Aug",
  keywords = "cameras, image registration, remote sensing, 3d reconstruction, mavmap, parallax-tolerant aerial image georegistration, pix4d, sfm method, structure from motion, visualsfm, camera metadata measurement, camera pose refinement, camera-induce",
  doi = "10.1109/TGRS.2017.2695172",
  url = "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7935425"
}

H. Aliakbarpour, K. Palaniappan, and G. Seetharaman. Parallax tolerant aerial image georegistration and efficient camera pose refinement - Without piecewise homographies. IEEE Transactions on Geoscience and Remote Sensing, volume 55, issue 8, pages 4618-4637, August 2017.

#263: H. Aliakbarpour, J. F. Ferreira, V. B. S. Prasath, K. Palaniappan, G. Seetharaman, and J. Dias

A probabilistic framework for 3D reconstruction using heterogeneous sensors

IEEE Sensors Journal, Volume 17, pgs. 2640-2641, 2017

3d reconstruction, depth sensor, sensor fusion

Abstract, Bibtex, PlainText, DOI, Google Scholar

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  author = "H. Aliakbarpour and J. F. Ferreira and V. B. S. Prasath and K. Palaniappan and G. Seetharaman and J. Dias",
  title = "A probabilistic framework for 3D reconstruction using heterogeneous sensors",
  year = 2017,
  journal = "IEEE Sensors Journal",
  volume = 17,
  number = 9,
  pages = "2640-2641",
  month = "May",
  keywords = "3d reconstruction, depth sensor, sensor fusion",
  doi = "10.1109/JSEN.2017.2679187"
}

H. Aliakbarpour, J. F. Ferreira, V. B. S. Prasath, K. Palaniappan, G. Seetharaman, and J. Dias. A probabilistic framework for 3D reconstruction using heterogeneous sensors. IEEE Sensors Journal, volume 17, issue 9, pages 2640-2641, May 2017.

#264: H. Aliakbarpour, J. F. Ferreira, V. B. S. Prasath, K. Palaniappan, G. Seetharaman, and J. Dias

A probabilistic framework for 3D reconstruction using heterogeneous sensors

IEEE Sensors, Volume 17, pgs. 1-2, 2017

3d reconstruction, depth sensor, sensor fusion

Abstract, Bibtex, PlainText, DOI, Google Scholar

@article{Hadi:kinect-fusion-IEEESensors2017,
  author = "H. Aliakbarpour and J. F. Ferreira and V. B. S. Prasath and K. Palaniappan and G. Seetharaman and J. Dias",
  title = "A probabilistic framework for 3D reconstruction using heterogeneous sensors",
  year = 2017,
  journal = "IEEE Sensors",
  volume = 17,
  number = 9,
  pages = "1-2",
  month = "May",
  keywords = "3d reconstruction, depth sensor, sensor fusion",
  doi = "10.1109/JSEN.2017.2679187"
}

H. Aliakbarpour, J. F. Ferreira, V. B. S. Prasath, K. Palaniappan, G. Seetharaman, and J. Dias. A probabilistic framework for 3D reconstruction using heterogeneous sensors. IEEE Sensors, volume 17, issue 9, pages 1-2, May 2017.

#262: A. Akay, H. AliAkbarpour, K. Palaniappan, and G. Seetharaman

Camera Auto-calibration for Planar Aerial Imagery Supported by Camera Metadata

Applied Imagery Pattern Recognition (AIPR), 2017

auto-calibration, planar scene reconstruction, metadata recovery, aerial imagery

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{2107a,
  author = "A. Akay and H. AliAkbarpour and K. Palaniappan and G. Seetharaman",
  title = "Camera Auto-calibration for Planar Aerial Imagery Supported by Camera Metadata",
  year = 2017,
  journal = "Applied Imagery Pattern Recognition (AIPR)",
  month = "Apr",
  keywords = "auto-calibration, planar scene reconstruction, metadata recovery, aerial imagery"
}

A. Akay, H. AliAkbarpour, K. Palaniappan, and G. Seetharaman. Camera Auto-calibration for Planar Aerial Imagery Supported by Camera Metadata. Applied Imagery Pattern Recognition (AIPR), April 2017.

#254: H. Aliakbarpour, K. Palaniappan, and G. Seetharaman

Oblique Aerial Image Georegistration and Efficient Camera Pose Refinement Without Correspondence-based Homographies

IEEE Transactions on Geoscience and Remote Sensing, 2017

registration, 3d reconstruction

Bibtex, PlainText, Google Scholar

@article{Hadi:oblique-IEEETGRS2017,
  author = "H. Aliakbarpour and K. Palaniappan and G. Seetharaman",
  title = "Oblique Aerial Image Georegistration and Efficient Camera Pose Refinement Without Correspondence-based Homographies",
  year = 2017,
  journal = "IEEE Transactions on Geoscience and Remote Sensing",
  month = "Apr",
  keywords = "registration, 3d reconstruction"
}

#256: J. Peng, A. J. Aved, G. Seetharaman, and K. Palaniappan

Multiview Boosting With Information Propagation for Classification

IEEE Transactions on Neural Networks and Learning Systems, 2017

biometrics, boosting, classification, convergence, data fusion, multiarmed bandits, multiview learning

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@article{Peng-TNNLS-2017,
  author = "J. Peng and A. J. Aved and G. Seetharaman and K. Palaniappan",
  title = "Multiview Boosting With Information Propagation for Classification",
  year = 2017,
  journal = "IEEE Transactions on Neural Networks and Learning Systems",
  month = "Feb",
  keywords = "biometrics, boosting, classification, convergence, data fusion, multiarmed bandits, multiview learning",
  doi = "10.1109/TNNLS.2016.2637881"
}

J. Peng, A. J. Aved, G. Seetharaman, and K. Palaniappan. Multiview Boosting With Information Propagation for Classification. IEEE Transactions on Neural Networks and Learning Systems, February 2017.

#260: H. Trinh, D. Chemodanov, S. Yao, Q. Lei, B. Zhang, F. Gao, P. Calyam, and K. Palaniappan

Energy-aware mobile edge computing for low-latency visual data processing

IEEE International Conference on Future Internet of Things and Cloud (FiCloud), 2017

iot-based applications, cloud computing, energy awareness, visual data processing, mobile edge cloud

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{2017a,
  author = "H. Trinh and D. Chemodanov and S. Yao and Q. Lei and B. Zhang and F. Gao and P. Calyam and K. Palaniappan",
  title = "Energy-aware mobile edge computing for low-latency visual data processing",
  year = 2017,
  journal = "IEEE International Conference on Future Internet of Things and Cloud (FiCloud)",
  month = "Jan",
  keywords = "iot-based applications, cloud computing, energy awareness, visual data processing, mobile edge cloud",
  url = "https://par.nsf.gov/servlets/purl/10048928"
}

H. Trinh, D. Chemodanov, S. Yao, Q. Lei, B. Zhang, F. Gao, P. Calyam, and K. Palaniappan. Energy-aware mobile edge computing for low-latency visual data processing. IEEE International Conference on Future Internet of Things and Cloud (FiCloud), January 2017.

#243: N. Al-Shakarji, F. Bunyak, G. Seetharaman, and K. Palaniappan

Vehicle tracking in wide area motion imagery using KC-LoFT multi-feature discriminative modeling

IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1-6, 2017

tracking, wide area motion imagery, kernelized correlation filter, discriminative modeling, ridge regression

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Noor2017:AIPR_KCLoFT,
  author = "N. Al-Shakarji and F. Bunyak and G. Seetharaman and K. Palaniappan",
  title = "Vehicle tracking in wide area motion imagery using KC-LoFT multi-feature discriminative modeling",
  year = 2017,
  booktitle = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  pages = "1-6",
  keywords = "tracking, wide area motion imagery, kernelized correlation filter, discriminative modeling, ridge regression",
  doi = "10.1109/AIPR.2017.8457953"
}

N. Al-Shakarji, F. Bunyak, G. Seetharaman, and K. Palaniappan. Vehicle tracking in wide area motion imagery using KC-LoFT multi-feature discriminative modeling. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1-6, 2017.

#252: S. Meena, P. Duraisamy, K. Palaniappan, and G. Seetharaman

Point based interactive image segmentation using multiquadrics splines

Proc. SPIE Conf. Pattern Recognition and Tracking XXVIII, Volume 10203, 2017

Abstract, Bibtex, PlainText, URL, DOI, Google Scholar

@inproceedings{2017a,
  author = "S. Meena and P. Duraisamy and K. Palaniappan and G. Seetharaman",
  title = "Point based interactive image segmentation using multiquadrics splines",
  year = 2017,
  journal = "Proc. SPIE Conf. Pattern Recognition and Tracking XXVIII",
  publisher = "SPIE Defense + Security",
  volume = 10203,
  doi = "https://doi.org/10.1117/12.2263933",
  url = "https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10203/102030C/Point-based-interactive-image-segmentation-using-multiquadrics-splines/10.1117/12.2263933.full?SSO=1"
}

S. Meena, P. Duraisamy, K. Palaniappan, and G. Seetharaman. Point based interactive image segmentation using multiquadrics splines. Proc. SPIE Conf. Pattern Recognition and Tracking XXVIII, SPIE Defense + Security, volume 10203, 2017.

#247: Z. A. Oraibi, M. Irio, A. Hafiane, and K. Palaniappan

Texture Classification using Multiple Local Descriptors

IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1-7, 2017

Abstract, Bibtex, PlainText, Google Scholar

@inproceedings{2017a,
  author = "Z. A. Oraibi and M. Irio and A. Hafiane and K. Palaniappan",
  title = "Texture Classification using Multiple Local Descriptors",
  year = 2017,
  journal = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  pages = "1-7"
}

Z. A. Oraibi, M. Irio, A. Hafiane, and K. Palaniappan. Texture Classification using Multiple Local Descriptors. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1-7, 2017.

#251: N. Al-Shakarji, F. Bunyak, G. Seetharaman, and K. Palaniappan

Robust multi-object tracking with semantic color correlation

IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pgs. 1-7, 2017

image color analysis, tracking, color, correlation, lighting, histograms.

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Noor2017:AVSS_ColorCorMTX,
  author = "N. Al-Shakarji and F. Bunyak and G. Seetharaman and K. Palaniappan",
  title = "Robust multi-object tracking with semantic color correlation",
  year = 2017,
  journal = "IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)",
  pages = "1-7",
  keywords = "image color analysis, tracking, color, correlation, lighting, histograms.",
  doi = "10.1109/AVSS.2017.8078507",
  url = "https://ieeexplore.ieee.org/abstract/document/8078507/"
}

N. Al-Shakarji, F. Bunyak, G. Seetharaman, and K. Palaniappan. Robust multi-object tracking with semantic color correlation. IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pages 1-7, 2017.

#245: S. Lyui, M. Chang, D. Du, L. Wen, H. Qi, Y. Li, Y. Wei, L. K., T. Hu, M. DelCoco, P. Carcagnì, D. Anisimov, E. Bochinski, F. Galasso, F. Bunyak, G. Han, H. Ye, H. Wang, K. Palaniappan, K. Ozcan, L. W. L. Wang, M. Lauer, N. Watcharapinchai, N. Song, N. Al-Shakarji, S. Wang, S. Amin, S. Rujikietgumjorn, T. Khanova, T. Sikora, T. Kutschbach, V. Eiselein, W. Tian, X. Xue, X. Yu, Y. Lu, Y. Zheng, Y. Huang, and Y. Zhang

UA-DETRAC 2017: Report of AVSS2017 & IWT4S challenge on advanced traffic monitoring

IEEE AVSS International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S), pgs. 1-7, 2017

multi-object tracking, tracking

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Noor2017:AVSS_UA_DETRAC_results,
  author = "S. Lyui and M. Chang and D. Du and L. Wen and H. Qi and Y. Li and Y. Wei and L. K. and T. Hu and M. DelCoco and P. Carcagnì and D. Anisimov and E. Bochinski and F. Galasso and F. Bunyak and G. Han and H. Ye and H. Wang and K. Palaniappan and K. Ozcan and L. W. L. Wang and M. Lauer and N. Watcharapinchai and N. Song and N. Al-Shakarji and S. Wang and S. Amin and S. Rujikietgumjorn and T. Khanova and T. Sikora and T. Kutschbach and V. Eiselein and W. Tian and X. Xue and X. Yu and Y. Lu and Y. Zheng and Y. Huang and Y. Zhang",
  title = "UA-DETRAC 2017: Report of AVSS2017 & IWT4S challenge on advanced traffic monitoring",
  year = 2017,
  journal = "IEEE AVSS International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S)",
  pages = "1-7",
  keywords = "multi-object tracking, tracking",
  doi = "10.1109/AVSS.2017.8078560",
  url = "https://ieeexplore.ieee.org/document/8078560"
}

S. Lyui, M. Chang, D. Du, L. Wen, H. Qi, Y. Li, Y. Wei, L. K., T. Hu, M. DelCoco, P. Carcagnì, D. Anisimov, E. Bochinski, F. Galasso, F. Bunyak, G. Han, H. Ye, H. Wang, K. Palaniappan, K. Ozcan, L. W. L. Wang, M. Lauer, N. Watcharapinchai, N. Song, N. Al-Shakarji, S. Wang, S. Amin, S. Rujikietgumjorn, T. Khanova, T. Sikora, T. Kutschbach, V. Eiselein, W. Tian, X. Xue, X. Yu, Y. Lu, Y. Zheng, Y. Huang, and Y. Zhang. UA-DETRAC 2017: Report of AVSS2017 & IWT4S challenge on advanced traffic monitoring. IEEE AVSS International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S), pages 1-7, 2017.

#246: M. Kristan, A. Leonardis, J. Matas, M. Felsberg, .., G. Seetharaman, K. Palaniappan, K. Gao, M. Poostchi, and e. al

The Visual Object Tracking VOT2017 challenge results

IEEE International Conference on Computer Vision Workshop (ICCVW), pgs. 1949-1972, 2017

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Kristan2017:ICCVW_VOT2017,
  author = "M. Kristan and A. Leonardis and J. Matas and M. Felsberg and .. and G. Seetharaman and K. Palaniappan and K. Gao and M. Poostchi and e. al",
  title = "The Visual Object Tracking VOT2017 challenge results",
  year = 2017,
  journal = "IEEE International Conference on Computer Vision Workshop (ICCVW)",
  pages = "1949-1972",
  doi = "10.1109/ICCVW.2017.230",
  url = "https://ieeexplore.ieee.org/document/8265440"
}

M. Kristan, A. Leonardis, J. Matas, M. Felsberg, .., G. Seetharaman, K. Palaniappan, K. Gao, M. Poostchi, and e. al. The Visual Object Tracking VOT2017 challenge results. IEEE International Conference on Computer Vision Workshop (ICCVW), pages 1949-1972, 2017.

#241: Z. Liang, S. Jaeger, G. Thoma, J. Huang, P. Guo, A. Powell, K. Silamut, I. Ersoy, M. Poostchi, K. Palaniappan, and R. J. Maude

CNN - Based Image Analysis for Malaria Diagnosis

Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2016

deep learning, malaira, cells, classification, biomedical

Abstract, Bibtex, PlainText, Google Scholar

@inproceedings{2016a,
  author = "Z. Liang and S. Jaeger and G. Thoma and J. Huang and P. Guo and A. Powell and K. Silamut and I. Ersoy and M. Poostchi and K. Palaniappan and R. J. Maude",
  title = "CNN - Based Image Analysis for Malaria Diagnosis",
  year = 2016,
  journal = "Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
  month = "Dec",
  keywords = "deep learning, malaira, cells, classification, biomedical"
}

Z. Liang, S. Jaeger, G. Thoma, J. Huang, P. Guo, A. Powell, K. Silamut, I. Ersoy, M. Poostchi, K. Palaniappan, and R. J. Maude. CNN - Based Image Analysis for Malaria Diagnosis. Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), December 2016.

#235: V. B. S. Prasath, Y. M. Kassim, Z. A. Oraibi, J.-B. Guiriec, A. Hafiane, G. Seetharaman, and K. Palaniappan

HEp-2 cell classification and segmentation using motif texture patterns and spatial features with random forests

Proc. IEEE International Conference on Pattern Recognition (ICPR), 2016

hep-2 cell classification, cell segmentation, texture features, motif patterns, random forests, segmentation, biomedical

Abstract, Bibtex, PlainText, URL, Google Scholar

@inproceedings{2016a,
  author = "V. B. S. Prasath and Y. M. Kassim and Z. A. Oraibi and J.-B. Guiriec and A. Hafiane and G. Seetharaman and K. Palaniappan",
  title = "HEp-2 cell classification and segmentation using motif texture patterns and spatial features with random forests",
  year = 2016,
  journal = "Proc. IEEE International Conference on Pattern Recognition (ICPR)",
  month = "Dec",
  keywords = "hep-2 cell classification, cell segmentation, texture features, motif patterns, random forests, segmentation, biomedical",
  url = "http://hep2.unisa.it/"
}

V. B. S. Prasath, Y. M. Kassim, Z. A. Oraibi, J.-B. Guiriec, A. Hafiane, G. Seetharaman, and K. Palaniappan. HEp-2 cell classification and segmentation using motif texture patterns and spatial features with random forests. Proc. IEEE International Conference on Pattern Recognition (ICPR), December 2016.

#230: K. Palaniappan, M. Poostchi, H. Aliakbarpour, R. Viguier, J. Fraser, F. Bunyak, A. Basharat, S. Suddarth, E. Blasch, R. Rao, and G. Seetharaman

Moving object detection for vehicle tracking in wide area motion imagery using 4D filtering

Proc. IEEE International Conference on Pattern Recognition (ICPR), 2016

wami, tracking

Abstract, Bibtex, PlainText, URL, Google Scholar

@inproceedings{Poostchi_2016_ICPR_Tracking,
  author = "K. Palaniappan and M. Poostchi and H. Aliakbarpour and R. Viguier and J. Fraser and F. Bunyak and A. Basharat and S. Suddarth and E. Blasch and R. Rao and G. Seetharaman",
  title = "Moving object detection for vehicle tracking in wide area motion imagery using 4D filtering",
  year = 2016,
  journal = "Proc. IEEE International Conference on Pattern Recognition (ICPR)",
  month = "Dec",
  keywords = "wami, tracking",
  url = "http://icpr2016.org/"
}

K. Palaniappan, M. Poostchi, H. Aliakbarpour, R. Viguier, J. Fraser, F. Bunyak, A. Basharat, S. Suddarth, E. Blasch, R. Rao, and G. Seetharaman. Moving object detection for vehicle tracking in wide area motion imagery using 4D filtering. Proc. IEEE International Conference on Pattern Recognition (ICPR), December 2016.

#219: R. Aktar, V. B. S. Prasath, H. Aliakbarpour, U. Sampathkumar, G. Seetharaman, and K. Palaniappan

Video haze removal and Poisson blending based mini-mosaics for wide area motion imagery

Proc. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1-7, 2016

haze removal, blending, mosaicing, wami

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{Rumana:Hazeremoval-WAMI-AIPR-2016,
  author = "R. Aktar and V. B. S. Prasath and H. Aliakbarpour and U. Sampathkumar and G. Seetharaman and K. Palaniappan",
  title = "Video haze removal and Poisson blending based mini-mosaics for wide area motion imagery",
  year = 2016,
  journal = "Proc. IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  publisher = "IEEE",
  pages = "1-7",
  month = "Oct",
  keywords = "haze removal, blending, mosaicing, wami",
  url = "https://www.researchgate.net/publication/319169936_Video_haze_removal_and_poisson_blending_based_mini-mosaics_for_wide_area_motion_imagery"
}

R. Aktar, V. B. S. Prasath, H. Aliakbarpour, U. Sampathkumar, G. Seetharaman, and K. Palaniappan. Video haze removal and Poisson blending based mini-mosaics for wide area motion imagery. Proc. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), IEEE, pages 1-7, October 2016.

#242: U. Sampathkumar, V. B. S. Prasath, S. Meena, and K. Palaniappan

Cloud-based interactive image segmentation using the FireFly visualization tool

Proc. IEEE Applied Imagery Pattern Recognition (AIPR), 2016

segmentation, interactive, gaussian elastic body, splines, firefly, visualization

Abstract, Bibtex, PlainText, Google Scholar

@inproceedings{Urmila:Interactive-Firefly-AIPR-2016,
  author = "U. Sampathkumar and V. B. S. Prasath and S. Meena and K. Palaniappan",
  title = "Cloud-based interactive image segmentation using the FireFly visualization tool",
  year = 2016,
  journal = "Proc. IEEE Applied Imagery Pattern Recognition (AIPR)",
  month = "Oct",
  keywords = "segmentation, interactive, gaussian elastic body, splines, firefly, visualization"
}

U. Sampathkumar, V. B. S. Prasath, S. Meena, and K. Palaniappan. Cloud-based interactive image segmentation using the FireFly visualization tool. Proc. IEEE Applied Imagery Pattern Recognition (AIPR), October 2016.

#240: Y. M. Kassim, V. B. S. Prasath, O. Glinskii, V. Glinsky, V. Huxley, and K. Palaniappan

Confocal vessel structure segmentation with optimized feature bank and random forests

Proc. IEEE Applied Imagery Pattern Recognition (AIPR), 2016

random forest, segmentation, vasculature, confocal, biomedical

Abstract, Bibtex, PlainText, Google Scholar

@inproceedings{Kassim:RandomForest-Confocal-AIPR-2016,
  author = "Y. M. Kassim and V. B. S. Prasath and O. Glinskii and V. Glinsky and V. Huxley and K. Palaniappan",
  title = "Confocal vessel structure segmentation with optimized feature bank and random forests",
  year = 2016,
  journal = "Proc. IEEE Applied Imagery Pattern Recognition (AIPR)",
  month = "Oct",
  keywords = "random forest, segmentation, vasculature, confocal, biomedical"
}

Y. M. Kassim, V. B. S. Prasath, O. Glinskii, V. Glinsky, V. Huxley, and K. Palaniappan. Confocal vessel structure segmentation with optimized feature bank and random forests. Proc. IEEE Applied Imagery Pattern Recognition (AIPR), October 2016.

#238: V. B. S. Prasath, S. Surineni, K. Gao, G. Seetharaman, and K. Palaniappan

CSANG: Continuous scale anisotropic Gaussians for robust linear structure extraction

Proc. IEEE Applied Imagery Pattern Recognition (AIPR), 2016

arst, edge detection, gpu

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Surya2016:AIPR_CSANG-edge,
  author = "V. B. S. Prasath and S. Surineni and K. Gao and G. Seetharaman and K. Palaniappan",
  title = "CSANG: Continuous scale anisotropic Gaussians for robust linear structure extraction",
  year = 2016,
  journal = "Proc. IEEE Applied Imagery Pattern Recognition (AIPR)",
  month = "Oct",
  keywords = "arst, edge detection, gpu",
  doi = "10.1109/AIPR.2016.8010551",
  url = "https://ieeexplore.ieee.org/abstract/document/8010551"
}

V. B. S. Prasath, S. Surineni, K. Gao, G. Seetharaman, and K. Palaniappan. CSANG: Continuous scale anisotropic Gaussians for robust linear structure extraction. Proc. IEEE Applied Imagery Pattern Recognition (AIPR), October 2016.

#222: G. Seetharaman, K. Palaniappan, F. Bunyak, and R. Wang

System and method for static and moving object detection

US Patent 9,454,819, 2016

flux tensor, object detection, ftsg, patent

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@patent{Guna:Patent_FTSG_2016,
  author = "G. Seetharaman and K. Palaniappan and F. Bunyak and R. Wang",
  title = "System and method for static and moving object detection",
  year = 2016,
  journal = "US Patent 9,454,819",
  month = "Sep",
  keywords = "flux tensor, object detection, ftsg, patent",
  url = "https://www.google.com/patents/US9454819"
}

G. Seetharaman, K. Palaniappan, F. Bunyak, and R. Wang. System and method for static and moving object detection. US Patent 9,454,819, September 2016.

#234: H. Aliakbarpour, V. B. S. Prasath, K. Palaniappan, G. Seetharaman, and J. Dias

Heterogeneous multi-view information fusion: Review of 3-D reconstruction methods and a new registration with uncertainty modeling

IEEE Access , Volume 4, pgs. 8264-8285, 2016

registration, 3d reconstruction, uncertainty modeling, sensor fusion

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@article{Hadi:multisensor-fusion-IEEEAccess2016,
  author = "H. Aliakbarpour and V. B. S. Prasath and K. Palaniappan and G. Seetharaman and J. Dias",
  title = "Heterogeneous multi-view information fusion: Review of 3-D reconstruction methods and a new registration with uncertainty modeling",
  year = 2016,
  journal = "IEEE Access ",
  volume = 4,
  number = 1,
  pages = "8264-8285",
  month = "Sep",
  keywords = "registration, 3d reconstruction, uncertainty modeling, sensor fusion",
  doi = "10.1109/ACCESS.2016.2629987"
}

H. Aliakbarpour, V. B. S. Prasath, K. Palaniappan, G. Seetharaman, and J. Dias. Heterogeneous multi-view information fusion: Review of 3-D reconstruction methods and a new registration with uncertainty modeling. IEEE Access , volume 4, issue 1, pages 8264-8285, September 2016.

#223: M. Poostchi, H. Aliakbarpour, R. Viguier, F. Bunyak, K. Palaniappan, and G. Seetharaman

Semantic depth map fusion for moving vehicle detection in aerial video

Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pgs. 32-40, 2016

wami, tracking, fmv, features, motion, image analysis

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{Poostchi_2016_CVPR_Workshops,
  author = "M. Poostchi and H. Aliakbarpour and R. Viguier and F. Bunyak and K. Palaniappan and G. Seetharaman",
  title = "Semantic depth map fusion for moving vehicle detection in aerial video",
  year = 2016,
  journal = "Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops",
  pages = "32-40",
  month = "Jul",
  keywords = "wami, tracking, fmv, features, motion, image analysis",
  url = "http://www.cv-foundation.org//openaccess/content_cvpr_2016_workshops/w25/papers/Poostchi_Semantic_Depth_Map_CVPR_2016_paper.pdf"
}

M. Poostchi, H. Aliakbarpour, R. Viguier, F. Bunyak, K. Palaniappan, and G. Seetharaman. Semantic depth map fusion for moving vehicle detection in aerial video. Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pages 32-40, July 2016.

#236: M. F. Pellechia, K. Palaniappan, P. J. Doucette, S. L. Dockstader, and G. S. (Editors)

Geospatial Informatics, Fusion and Motion Video Analytics VI

Proceedings of SPIE, Volume 9473, pgs. 256 , 2016

Bibtex, PlainText, Google Scholar

@book{2016a,
  author = "M. F. Pellechia and K. Palaniappan and P. J. Doucette and S. L. Dockstader and G. S. (Editors)",
  title = "Geospatial Informatics, Fusion and Motion Video Analytics VI",
  year = 2016,
  journal = "Proceedings of SPIE",
  volume = 9473,
  pages = "256 ",
  month = "Jun"
}

#226: J. Kwon, A. Wang, D. J. Burke, H. E. Boudreau, K. J. Lekstrom, A. Korzeniowska, R. Sugamata, Y.-S. Kim, L. Yi, I. Ersoy, S. Jaeger, K. Palaniappan, D. R. Ambruso, S. H. Jackson, and T. L. Leto

Peroxiredoxin 6 (Prdx6) supports NADPH oxidase1 (Nox1)-based superoxide generation and cell migration

Free Radical Biology and Medicine, 2016

cell migration, biomedical

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@article{Kwon-Cell-Migration-FRBM2016,
  author = "J. Kwon and A. Wang and D. J. Burke and H. E. Boudreau and K. J. Lekstrom and A. Korzeniowska and R. Sugamata and Y.-S. Kim and L. Yi and I. Ersoy and S. Jaeger and K. Palaniappan and D. R. Ambruso and S. H. Jackson and T. L. Leto",
  title = "Peroxiredoxin 6 (Prdx6) supports NADPH oxidase1 (Nox1)-based superoxide generation and cell migration",
  year = 2016,
  journal = "Free Radical Biology and Medicine",
  month = "Apr",
  keywords = "cell migration, biomedical",
  doi = "10.1016/j.freeradbiomed.2016.04.009"
}

J. Kwon, A. Wang, D. J. Burke, H. E. Boudreau, K. J. Lekstrom, A. Korzeniowska, R. Sugamata, Y.-S. Kim, L. Yi, I. Ersoy, S. Jaeger, K. Palaniappan, D. R. Ambruso, S. H. Jackson, and T. L. Leto. Peroxiredoxin 6 (Prdx6) supports NADPH oxidase1 (Nox1)-based superoxide generation and cell migration. Free Radical Biology and Medicine, April 2016.

#218: M. Kristan, A. Leonardis, J. Matas, M. Felsberg, R. Pflugfelder, L. Cehovin, T. Vojir, G. Häger, A. Lukezic, G. Fernandez, A. Gupta, A. Petrosino, A. Memarmoghadam, A. Garcia-Martin, A. S. Montero, A. Vedaldi, A. Robinson, A. J. Ma, A. Varfolomieiev, A. Alatan, A. Erdem, B. Ghanem, B. Liu, B. Han, B. Martinez, C.-M. Chang, C. Xu, C. Sun, D. Kim, D. Chen, D. Du, D. Mishra, D.-Y. Yeung, E. Gundogdu, E. Erdem, F. Khan, F. Porikli, F. Zhao, F. Bunyak, F. Battistone, G. Zhu, G. Roffo, G. R. K. S. Subrahmanyam, G. Bastos, G. Seetharaman, H. Medeiros, H. Li, H. Qi, H. Bischof, H. Possegger, H. Lu, H. Lee, H. Nam, H. J. Chang, I. Drummond, J. Valmadre, J.-c. Jeong, J.-i. Cho, J.-Y. Lee, J. Zhu, J. Feng, J. Gao, J. Y. Choi, J. Xiao, J.-W. Kim, J. Jeong, J. F. Henriques, J. Lang, J. Choi, J. M. Martinez, J. Xing, J. Gao, K. Palaniappan, K. Lebeda, K. Gao, K. Mikolajczyk, L. Qin, L. Wang, L. Wen, L. Bertinetto, M. K. Rapuru, M. Poostchi, M. Maresca, M. Danelljan, M. Mueller, M. Zhang, M. Arens, M. Valstar, M. Tang, M. Baek, M. H. Khan, N. Wang, N. Fan, N. Al-Shakarji, O. Miksik, O. Akin, P. Moallem, P. Senna, P. H. S. Torr, P. C. Yuen, Q. Huang, R. Martin-Nieto, R. Pelapur, R. Bowden, R. Laganière, R. Stolkin, R. Walsh, S. B. Krah, S. Li, S. Zhang, S. Yao, S. Hadfield, S. Melzi, S. Lyu, S. Li, S. Becker, S. Golodetz, S. Kakanuru, S. Choi, T. Hu, T. Mauthner, T. Zhang, T. Pridmore, V. Santopietro, W. Hu, W. Li, W. Hübner, X. Lan, X. Wang, X. Li, Y. Li, Y. Demiris, Y. Wang, Y. Qi, Z. Yuan, Z. Cai, Z. Xu, Z. He, and Z. Chi

The visual object tracking VOT2016 challenge results

European Conference on Computer Vision Workshop (ECCVW), pgs. 777--823, 2016

performance, evaluation, short-term, single-object trackers, vot

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{Noor:ECCV_VOT_report,
author = "M. Kristan and A. Leonardis and J. Matas and M. Felsberg and R. Pflugfelder and L. Cehovin and T. Vojir and G. Häger and A. Lukezic and G. Fernandez and A. Gupta and A. Petrosino and A. Memarmoghadam and A. Garcia-Martin and A. S. Montero and A. Vedaldi and A. Robinson and A. J. Ma and A. Varfolomieiev and A. Alatan and A. Erdem and B. Ghanem and B. Liu and B. Han and B. Martinez and C.-M. Chang and C. Xu and C. Sun and D. Kim and D. Chen and D. Du and D. Mishra and D.-Y. Yeung and E. Gundogdu and E. Erdem and F. Khan and F. Porikli and F. Zhao and F. Bunyak and F. Battistone and G. Zhu and G. Roffo and G. R. K. S. Subrahmanyam and G. Bastos and G. Seetharaman and H. Medeiros and H. Li and H. Qi and H. Bischof and H. Possegger and H. Lu and H. Lee and H. Nam and H. J. Chang and I. Drummond and J. Valmadre and J.-c. Jeong and J.-i. Cho and J.-Y. Lee and J. Zhu and J. Feng and J. Gao and J. Y. Choi and J. Xiao and J.-W. Kim and J. Jeong and J. F. Henriques and J. Lang and J. Choi and J. M. Martinez and J. Xing and J. Gao and K. Palaniappan and K. Lebeda and K. Gao and K. Mikolajczyk and L. Qin and L. Wang and L. Wen and L. Bertinetto and M. K. Rapuru and M. Poostchi and M. Maresca and M. Danelljan and M. Mueller and M. Zhang and M. Arens and M. Valstar and M. Tang and M. Baek and M. H. Khan and N. Wang and N. Fan and N. Al-Shakarji and O. Miksik and O. Akin and P. Moallem and P. Senna and P. H. S. Torr and P. C. Yuen and Q. Huang and R. Martin-Nieto and R. Pelapur and R. Bowden and R. Laganière and R. Stolkin and R. Walsh and S. B. Krah and S. Li and S. Zhang and S. Yao and S. Hadfield and S. Melzi and S. Lyu and S. Li and S. Becker and S. Golodetz and S. Kakanuru and S. Choi and T. Hu and T. Mauthner and T. Zhang and T. Pridmore and V. Santopietro and W. Hu and W. Li and W. Hübner and X. Lan and X. Wang and X. Li and Y. Li and Y. Demiris and Y. Wang and Y. Qi and Z. Yuan and Z. Cai and Z. Xu and Z. He and Z. Chi",
title = "The visual object tracking VOT2016 challenge results",
year = 2016,
booktitle = "European Conference on Computer Vision Workshop (ECCVW)",
pages = "777--823",
keywords = "performance, evaluation, short-term, single-object trackers, vot",
url = "https://link.springer.com/chapter/10.1007/978-3-319-48881-3_54"
}

M. Kristan, A. Leonardis, J. Matas, M. Felsberg, R. Pflugfelder, L. Cehovin, T. Vojir, G. Häger, A. Lukezic, G. Fernandez, A. Gupta, A. Petrosino, A. Memarmoghadam, A. Garcia-Martin, A. S. Montero, A. Vedaldi, A. Robinson, A. J. Ma, A. Varfolomieiev, A. Alatan, A. Erdem, B. Ghanem, B. Liu, B. Han, B. Martinez, C.-M. Chang, C. Xu, C. Sun, D. Kim, D. Chen, D. Du, D. Mishra, D.-Y. Yeung, E. Gundogdu, E. Erdem, F. Khan, F. Porikli, F. Zhao, F. Bunyak, F. Battistone, G. Zhu, G. Roffo, G. R. K. S. Subrahmanyam, G. Bastos, G. Seetharaman, H. Medeiros, H. Li, H. Qi, H. Bischof, H. Possegger, H. Lu, H. Lee, H. Nam, H. J. Chang, I. Drummond, J. Valmadre, J.-c. Jeong, J.-i. Cho, J.-Y. Lee, J. Zhu, J. Feng, J. Gao, J. Y. Choi, J. Xiao, J.-W. Kim, J. Jeong, J. F. Henriques, J. Lang, J. Choi, J. M. Martinez, J. Xing, J. Gao, K. Palaniappan, K. Lebeda, K. Gao, K. Mikolajczyk, L. Qin, L. Wang, L. Wen, L. Bertinetto, M. K. Rapuru, M. Poostchi, M. Maresca, M. Danelljan, M. Mueller, M. Zhang, M. Arens, M. Valstar, M. Tang, M. Baek, M. H. Khan, N. Wang, N. Fan, N. Al-Shakarji, O. Miksik, O. Akin, P. Moallem, P. Senna, P. H. S. Torr, P. C. Yuen, Q. Huang, R. Martin-Nieto, R. Pelapur, R. Bowden, R. Laganière, R. Stolkin, R. Walsh, S. B. Krah, S. Li, S. Zhang, S. Yao, S. Hadfield, S. Melzi, S. Lyu, S. Li, S. Becker, S. Golodetz, S. Kakanuru, S. Choi, T. Hu, T. Mauthner, T. Zhang, T. Pridmore, V. Santopietro, W. Hu, W. Li, W. Hübner, X. Lan, X. Wang, X. Li, Y. Li, Y. Demiris, Y. Wang, Y. Qi, Z. Yuan, Z. Cai, Z. Xu, Z. He, and Z. Chi. The visual object tracking VOT2016 challenge results. European Conference on Computer Vision Workshop (ECCVW), pages 777--823, 2016.

#217: M. Felsberg, M. Kristan, J. Matas, A. Leonardis, R. Pflugfelder, G. Häger, A. Berg, A. Eldesokey, J. Ahlberg, L. Cehovin, T. Vojir, A. Lukezic, G. Fernandez, A. Petrosino, A. Garcia-Martin, A. S. Montero, A. Varfolomieiev, A. Erdem, B. Han, C.-M. Chang, D. Du, E. Erdem, F. S. Khan, F. Porikli, F. Zhao, F. Bunyak, F. Battistone, G. Zhu, G. Seetharaman, H. Li, H. Qi, H. Bischof, H. Possegger, H. Nam, J. Valmadre, J. Zhu, J. Feng, J. Lang, J. M. Martinez, K. Palaniappan, K. Lebeda, K. Gao, K. Mikolajczyk, L. Wen, L. Bertinetto, M. Poostchi, M. Maresca, M. Danelljan, M. Arens, M. Tang, M. Baek, N. Fan, N. Al-Shakarji, O. Miksik, O. Akin, P. H. S. Torr, Q. Huang, R. Martin-Nieto, R. Pelapur, R. Bowden, R. Laganière, S. B. Krah, S. Li, S. Yao, S. Hadfield, S. Lyu, S. Becker, S. Golodetz, T. Hu, T. Mauthner, V. Santopietro, W. Li, W. Hübner, X. Li, Y. Li, Z. Xu, and Z. He

The thermal infrared visual object tracking VOT-TIR2016 challenge results

European Conference on Computer Vision Workshop (ECCVW), pgs. 824--849, 2016

performance, evaluation, object tracking, thermal, ir, vot

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{Noor2016:ECCV_Thermal_VOT_report,
author = "M. Felsberg and M. Kristan and J. Matas and A. Leonardis and R. Pflugfelder and G. Häger and A. Berg and A. Eldesokey and J. Ahlberg and L. Cehovin and T. Vojir and A. Lukezic and G. Fernandez and A. Petrosino and A. Garcia-Martin and A. S. Montero and A. Varfolomieiev and A. Erdem and B. Han and C.-M. Chang and D. Du and E. Erdem and F. S. Khan and F. Porikli and F. Zhao and F. Bunyak and F. Battistone and G. Zhu and G. Seetharaman and H. Li and H. Qi and H. Bischof and H. Possegger and H. Nam and J. Valmadre and J. Zhu and J. Feng and J. Lang and J. M. Martinez and K. Palaniappan and K. Lebeda and K. Gao and K. Mikolajczyk and L. Wen and L. Bertinetto and M. Poostchi and M. Maresca and M. Danelljan and M. Arens and M. Tang and M. Baek and N. Fan and N. Al-Shakarji and O. Miksik and O. Akin and P. H. S. Torr and Q. Huang and R. Martin-Nieto and R. Pelapur and R. Bowden and R. Laganière and S. B. Krah and S. Li and S. Yao and S. Hadfield and S. Lyu and S. Becker and S. Golodetz and T. Hu and T. Mauthner and V. Santopietro and W. Li and W. Hübner and X. Li and Y. Li and Z. Xu and Z. He",
title = "The thermal infrared visual object tracking VOT-TIR2016 challenge results",
year = 2016,
booktitle = "European Conference on Computer Vision Workshop (ECCVW)",
pages = "824--849",
keywords = "performance, evaluation, object tracking, thermal, ir, vot",
url = "https://link.springer.com/chapter/10.1007/978-3-319-48881-3_55"
}

M. Felsberg, M. Kristan, J. Matas, A. Leonardis, R. Pflugfelder, G. Häger, A. Berg, A. Eldesokey, J. Ahlberg, L. Cehovin, T. Vojir, A. Lukezic, G. Fernandez, A. Petrosino, A. Garcia-Martin, A. S. Montero, A. Varfolomieiev, A. Erdem, B. Han, C.-M. Chang, D. Du, E. Erdem, F. S. Khan, F. Porikli, F. Zhao, F. Bunyak, F. Battistone, G. Zhu, G. Seetharaman, H. Li, H. Qi, H. Bischof, H. Possegger, H. Nam, J. Valmadre, J. Zhu, J. Feng, J. Lang, J. M. Martinez, K. Palaniappan, K. Lebeda, K. Gao, K. Mikolajczyk, L. Wen, L. Bertinetto, M. Poostchi, M. Maresca, M. Danelljan, M. Arens, M. Tang, M. Baek, N. Fan, N. Al-Shakarji, O. Miksik, O. Akin, P. H. S. Torr, Q. Huang, R. Martin-Nieto, R. Pelapur, R. Bowden, R. Laganière, S. B. Krah, S. Li, S. Yao, S. Hadfield, S. Lyu, S. Becker, S. Golodetz, T. Hu, T. Mauthner, V. Santopietro, W. Li, W. Hübner, X. Li, Y. Li, Z. Xu, and Z. He. The thermal infrared visual object tracking VOT-TIR2016 challenge results. European Conference on Computer Vision Workshop (ECCVW), pages 824--849, 2016.

#233: Z. Al-Milaji, I. Ersoy, K. Palaniappan, and F. Bunyak

Hybrid Deep Learning Framework for the Classification of Epithelial and Stromal Tissues in H&E Images

Proc. First International Workshop on Deep Learning for Pattern Recognition (DLPR), 2016

deep learning, histopathology, segmentation

Bibtex, PlainText, URL, Google Scholar

@inproceedings{Al-Milaji:histopathology-deeplearning-2016a,
  author = "Z. Al-Milaji and I. Ersoy and K. Palaniappan and F. Bunyak",
  title = "Hybrid Deep Learning Framework for the Classification of Epithelial and Stromal Tissues in H&E Images",
  year = 2016,
  journal = "Proc. First International Workshop on Deep Learning for Pattern Recognition (DLPR)",
  keywords = "deep learning, histopathology, segmentation",
  url = "http://dlpr2016.csp.escience.cn/dct/page/70005"
}

#239: N. Al-Shakarji, F. Bunyak, and K. Palaniappan

CS-LOFT: Color and scale adaptive tracking using Bhattacharyya distance and max pooling consistency

IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2016

color, tracking, features, single object tracking

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Noor:Color-LOFT-AIPR-2016,
  author = "N. Al-Shakarji and F. Bunyak and K. Palaniappan",
  title = "CS-LOFT: Color and scale adaptive tracking using Bhattacharyya distance and max pooling consistency",
  year = 2016,
  journal = "IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
  keywords = "color, tracking, features, single object tracking",
  doi = "10.1109/AIPR.2016.8010604",
  url = "https://ieeexplore.ieee.org/abstract/document/8010604"
}

N. Al-Shakarji, F. Bunyak, and K. Palaniappan. CS-LOFT: Color and scale adaptive tracking using Bhattacharyya distance and max pooling consistency. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2016.

#220: S. Meena, K. Palaniappan, and G. Seetharaman

User driven sparse point based image segmentation

IEEE International Conference on Image Processing (ICIP), pgs. 844-848, 2016

segmentation, interactive, gaussian elastic body, splines

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Meena-interactive-ICIP2016,
  author = "S. Meena and K. Palaniappan and G. Seetharaman",
  title = "User driven sparse point based image segmentation",
  year = 2016,
  journal = "IEEE International Conference on Image Processing (ICIP)",
  pages = "844-848",
  keywords = "segmentation, interactive, gaussian elastic body, splines",
  doi = "10.1109/ICIP.2016.7532476"
}

S. Meena, K. Palaniappan, and G. Seetharaman. User driven sparse point based image segmentation. IEEE International Conference on Image Processing (ICIP), pages 844-848, 2016.

#221: V. B. S. Prasath, R. Pelapur, K. Palaniappan, and G. Seetharaman

Textured object video segmentation using temporal coherency

Proc. SPIE Conf. Geospatial Informatics, Fusion, and Motion Video Analytics VI, 2016

segmentation, videos, active contours

Abstract, Bibtex, PlainText, Google Scholar

@inproceedings{Prasath:Videoseg-SPIE-2016,
  author = "V. B. S. Prasath and R. Pelapur and K. Palaniappan and G. Seetharaman",
  title = "Textured object video segmentation using temporal coherency",
  year = 2016,
  journal = "Proc. SPIE Conf. Geospatial Informatics, Fusion, and Motion Video Analytics VI",
  keywords = "segmentation, videos, active contours"
}

V. B. S. Prasath, R. Pelapur, K. Palaniappan, and G. Seetharaman. Textured object video segmentation using temporal coherency. Proc. SPIE Conf. Geospatial Informatics, Fusion, and Motion Video Analytics VI, 2016.

#224: P. Calyam, D. Chemodanov, R. Pelapur, and K. Palaniappan

Regional-scale incident-supporting visual cloud computing with software-defined networking

Proc. SPIE Conf. Geospatial Informatics, Fusion, and Motion Video Analytics VI, 2016

cloud computing, resource management, tracking

Abstract, Bibtex, PlainText, Google Scholar

@inproceedings{Calyam:Cloud-SPIE-2016,
  author = "P. Calyam and D. Chemodanov and R. Pelapur and K. Palaniappan",
  title = "Regional-scale incident-supporting visual cloud computing with software-defined networking",
  year = 2016,
  journal = "Proc. SPIE Conf. Geospatial Informatics, Fusion, and Motion Video Analytics VI",
  keywords = "cloud computing, resource management, tracking"
}

P. Calyam, D. Chemodanov, R. Pelapur, and K. Palaniappan. Regional-scale incident-supporting visual cloud computing with software-defined networking. Proc. SPIE Conf. Geospatial Informatics, Fusion, and Motion Video Analytics VI, 2016.

#232: R. Gargees, B. Morago, R. Pelapur, D. Chemodanov, P. Calyam, Z. Oraibi, Y. Duan, G. Seetharaman, and K. Palaniappan

Incident-supporting visual cloud computing utilizing software-defined networking

IEEE Transactions on Circuits and Systems for Video Technology, 2016

cloud computing, resource management, tracking

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

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  title = "Incident-supporting visual cloud computing utilizing software-defined networking",
  year = 2016,
  journal = "IEEE Transactions on Circuits and Systems for Video Technology",
  keywords = "cloud computing, resource management, tracking",
  doi = "10.1109/TCSVT.2016.2564898"
}

R. Gargees, B. Morago, R. Pelapur, D. Chemodanov, P. Calyam, Z. Oraibi, Y. Duan, G. Seetharaman, and K. Palaniappan. Incident-supporting visual cloud computing utilizing software-defined networking. IEEE Transactions on Circuits and Systems for Video Technology, 2016.

#231: S. Meena, K. Palaniappan, G. Seetharaman, and V. B. S. Prasath

Interactive target selection in aerial imagery using elastic body splines

Proc. SPIE Conf. Geospatial Informatics, Fusion, and Motion Video Analytics VI, 2016

interactive, segmentation, wami, target selection

Abstract, Bibtex, PlainText, Google Scholar

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  title = "Interactive target selection in aerial imagery using elastic body splines",
  year = 2016,
  journal = "Proc. SPIE Conf. Geospatial Informatics, Fusion, and Motion Video Analytics VI",
  keywords = "interactive, segmentation, wami, target selection"
}

S. Meena, K. Palaniappan, G. Seetharaman, and V. B. S. Prasath. Interactive target selection in aerial imagery using elastic body splines. Proc. SPIE Conf. Geospatial Informatics, Fusion, and Motion Video Analytics VI, 2016.

#228: S. Meena, R. Pelapur, V. B. S. Prasath, and K. Palaniappan

Multiscale focus driven segmentation using elastic body splines

Proc. SPIE Conf. Geospatial Informatics, Fusion, and Motion Video Analytics VI, 2016

interactive, segmentation, focus

Abstract, Bibtex, PlainText, Google Scholar

@inproceedings{Meena:Focusseg-SPIE-2016,
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  title = "Multiscale focus driven segmentation using elastic body splines",
  year = 2016,
  journal = "Proc. SPIE Conf. Geospatial Informatics, Fusion, and Motion Video Analytics VI",
  keywords = "interactive, segmentation, focus"
}

S. Meena, R. Pelapur, V. B. S. Prasath, and K. Palaniappan. Multiscale focus driven segmentation using elastic body splines. Proc. SPIE Conf. Geospatial Informatics, Fusion, and Motion Video Analytics VI, 2016.

#229: S. Meena, V. B. S. Prasath, Y. M. Kassim, R. J. Maude, O. Glinskii, V. Glinsky, V. Huxley, and K. Palaniappan

Multiquadric spline-based interactive segmentation of vascular networks

38th IEEE Engineering in Medicine and Biology Society Conf. (EMBC), pgs. 5913-5916, 2016

interactive, segmentation, vasculature, biomedical

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

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  title = "Multiquadric spline-based interactive segmentation of vascular networks",
  year = 2016,
  journal = "38th IEEE Engineering in Medicine and Biology Society Conf. (EMBC)",
  pages = "5913-5916",
  keywords = "interactive, segmentation, vasculature, biomedical",
  doi = "10.1109/EMBC.2016.7592074"
}

S. Meena, V. B. S. Prasath, Y. M. Kassim, R. J. Maude, O. Glinskii, V. Glinsky, V. Huxley, and K. Palaniappan. Multiquadric spline-based interactive segmentation of vascular networks. 38th IEEE Engineering in Medicine and Biology Society Conf. (EMBC), pages 5913-5916, 2016.

#225: Y. M. Kassim, V. B. S. Prasath, R. Pelapur, O. Glinskii, R. J. Maude, V. Glinsky, V. Huxley, and K. Palaniappan

Random forests for dura mater microvasculature segmentation using epifluorescence images

38th IEEE Engineering in Medicine and Biology Society Conf. (EMBC), pgs. 2901-2904, 2016

random forest, segmentation, vasculature, biomedical

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Kassim:RandomForest-EMBS-2016,
  author = "Y. M. Kassim and V. B. S. Prasath and R. Pelapur and O. Glinskii and R. J. Maude and V. Glinsky and V. Huxley and K. Palaniappan",
  title = "Random forests for dura mater microvasculature segmentation using epifluorescence images",
  year = 2016,
  journal = "38th IEEE Engineering in Medicine and Biology Society Conf. (EMBC)",
  pages = "2901-2904",
  keywords = "random forest, segmentation, vasculature, biomedical",
  doi = "10.1109/EMBC.2016.7591336"
}

Y. M. Kassim, V. B. S. Prasath, R. Pelapur, O. Glinskii, R. J. Maude, V. Glinsky, V. Huxley, and K. Palaniappan. Random forests for dura mater microvasculature segmentation using epifluorescence images. 38th IEEE Engineering in Medicine and Biology Society Conf. (EMBC), pages 2901-2904, 2016.

#202: S. Meena, K. Palaniappan, and G. Seetharaman

Interactive image segmentation using elastic interpolation

IEEE International Symposium on Multimedia (ISM), pgs. 307-310 , 2015

segmentation, interactive, elastic body, splines

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Sachin_Interactive_ISM2016,
  author = "S. Meena and K. Palaniappan and G. Seetharaman",
  title = "Interactive image segmentation using elastic interpolation",
  year = 2015,
  journal = "IEEE International Symposium on Multimedia (ISM)",
  pages = "307-310 ",
  month = "Dec",
  keywords = "segmentation, interactive, elastic body, splines",
  doi = "10.1109/ISM.2015.78"
}

S. Meena, K. Palaniappan, and G. Seetharaman. Interactive image segmentation using elastic interpolation. IEEE International Symposium on Multimedia (ISM), pages 307-310 , December 2015.

#216: W. R. Thissell, R. Czajkowski, F. Schrenk, T. Selway, A. J. Ries, S. Patel, P. L. McDermott, R. Moten, R. Rudnicki, G. Seetharaman, I. Ersoy, and K. Palaniappan

A scalable architecture for operational FMV exploitation (AVAA)

Proc. IEEE International Conference on Computer Vision Workshop (ICCVW) Video Summarization for Large-scale Analytics Workshop, 2015

fmv, motion, features, image anlaysis, wami

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Thissell:FMV-Exploitation-ICCVW-2016,
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  title = "A scalable architecture for operational FMV exploitation (AVAA)",
  year = 2015,
  journal = "Proc. IEEE International Conference on Computer Vision Workshop (ICCVW) Video Summarization for Large-scale Analytics Workshop",
  month = "Dec",
  keywords = "fmv, motion, features, image anlaysis, wami",
  doi = "10.1109/ICCVW.2015.139",
  url = "https://sites.google.com/site/lsvsiccv2015/video-summarization-for-large-scale-analytics-workshop"
}

W. R. Thissell, R. Czajkowski, F. Schrenk, T. Selway, A. J. Ries, S. Patel, P. L. McDermott, R. Moten, R. Rudnicki, G. Seetharaman, I. Ersoy, and K. Palaniappan. A scalable architecture for operational FMV exploitation (AVAA). Proc. IEEE International Conference on Computer Vision Workshop (ICCVW) Video Summarization for Large-scale Analytics Workshop, December 2015.

#205: H. AliAkbarpour, K. Palaniappan, and G. Seetharaman

Fast structure from motion for sequential and wide area motion imagery

Proc. IEEE International Conference on Computer Vision Workshop (ICCVW) Video Summarization for Large-scale Analytics Workshop, 2015

structure from motion, 3d reconstruction, bundle adjustment, wami

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Hadi:SfM-ICCVW-2015,
  author = "H. AliAkbarpour and K. Palaniappan and G. Seetharaman",
  title = "Fast structure from motion for sequential and wide area motion imagery",
  year = 2015,
  journal = "Proc. IEEE International Conference on Computer Vision Workshop (ICCVW) Video Summarization for Large-scale Analytics Workshop",
  month = "Dec",
  keywords = "structure from motion, 3d reconstruction, bundle adjustment, wami",
  doi = "10.1109/ICCVW.2015.142",
  url = "https://sites.google.com/site/lsvsiccv2015/video-summarization-for-large-scale-analytics-workshop"
}

H. AliAkbarpour, K. Palaniappan, and G. Seetharaman. Fast structure from motion for sequential and wide area motion imagery. Proc. IEEE International Conference on Computer Vision Workshop (ICCVW) Video Summarization for Large-scale Analytics Workshop, December 2015.

#193: M. Kristan, J. Matas, A. Leonardis, M. Felsberg, .., R. Pelapur, K. Palaniappan, F. Bunyak, M. Poostchi, S. Yao, and K. Gao

The Visual Object Tracking VOT2015 challenge results

IEEE International Conference on Computer Vision Workshops (ICCVW), pgs. 564-586, 2015

wami, tracking, fmv, features, motion, image analysis

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Kristan2015:ICCVW_VOT2015,
  author = "M. Kristan and J. Matas and A. Leonardis and M. Felsberg and .. and R. Pelapur and K. Palaniappan and F. Bunyak and M. Poostchi and S. Yao and K. Gao",
  title = "The Visual Object Tracking VOT2015 challenge results",
  year = 2015,
  journal = "IEEE International Conference on Computer Vision Workshops (ICCVW)",
  pages = "564-586",
  month = "Dec",
  keywords = "wami, tracking, fmv, features, motion, image analysis",
  doi = "10.1109/ICCVW.2015.79",
  url = "http://www.votchallenge.net/vot2015/program.html"
}

M. Kristan, J. Matas, A. Leonardis, M. Felsberg, .., R. Pelapur, K. Palaniappan, F. Bunyak, M. Poostchi, S. Yao, and K. Gao. The Visual Object Tracking VOT2015 challenge results. IEEE International Conference on Computer Vision Workshops (ICCVW), pages 564-586, December 2015.

#209: R. Viguier, C. C. Lin, H. AliAkbarpour, F. Bunyak, S. Pankanti, G. Seetharaman, and K. Palaniappan

Automatic video content summarization using geospatial mosaics of aerial imagery

IEEE International Symposium on Multimedia (ISM), pgs. 5pp, 2015

video summarization, mosaicing, geospatial

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Raphael:summarization-ISM-2015,,
  author = "R. Viguier and C. C. Lin and H. AliAkbarpour and F. Bunyak and S. Pankanti and G. Seetharaman and K. Palaniappan",
  title = "Automatic video content summarization using geospatial mosaics of aerial imagery",
  year = 2015,
  journal = "IEEE International Symposium on Multimedia (ISM)",
  pages = "5pp",
  month = "Dec",
  keywords = "video summarization, mosaicing, geospatial",
  doi = "10.1109/ISM.2015.124",
  url = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7440962"
}

R. Viguier, C. C. Lin, H. AliAkbarpour, F. Bunyak, S. Pankanti, G. Seetharaman, and K. Palaniappan. Automatic video content summarization using geospatial mosaics of aerial imagery. IEEE International Symposium on Multimedia (ISM), pages 5pp, December 2015.

#195: H. Aliakbarpour, K. Palaniappan, and G. Seetharaman

Robust camera pose refinement and rapid SfM for multiview aerial imagery—Without RANSAC

IEEE Geoscience and Remote Sensing Letters, Volume 12, pgs. 2203-2207 , 2015

3d reconstruction, wami, shape from motion

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@article{Hadi:SfM_GRSL-2013,
  author = "H. Aliakbarpour and K. Palaniappan and G. Seetharaman",
  title = "Robust camera pose refinement and rapid SfM for multiview aerial imagery—Without RANSAC",
  year = 2015,
  journal = "IEEE Geoscience and Remote Sensing Letters",
  volume = 12,
  number = 11,
  pages = "2203-2207 ",
  month = "Nov",
  keywords = "3d reconstruction, wami, shape from motion",
  doi = "10.1109/LGRS.2015.2457299",
  url = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7185357"
}

H. Aliakbarpour, K. Palaniappan, and G. Seetharaman. Robust camera pose refinement and rapid SfM for multiview aerial imagery—Without RANSAC. IEEE Geoscience and Remote Sensing Letters, volume 12, issue 11, pages 2203-2207 , November 2015.

#196: R. Viguier, C. C. Lin, K. Swaminathan, A. Vega, A. Buyuktosunoglu, S. Pankanti, P. Bose, H. Akbarpour, F. Bunyak, K. Palaniappan, and G. Seetharaman

Resilient mobile cognition: Algorithms, innovations, and architectures

IEEE International Conference on Computer Design (ICCD), pgs. 4pp, 2015

video summarization, mosaicing, geospatial, mobile, cognition

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{Raphael:Mobile-ICCD-2015,
  author = "R. Viguier and C. C. Lin and K. Swaminathan and A. Vega and A. Buyuktosunoglu and S. Pankanti and P. Bose and H. Akbarpour and F. Bunyak and K. Palaniappan and G. Seetharaman",
  title = "Resilient mobile cognition: Algorithms, innovations, and architectures",
  year = 2015,
  journal = "IEEE International Conference on  Computer Design (ICCD)",
  pages = "4pp",
  month = "Oct",
  keywords = "video summarization, mosaicing, geospatial, mobile, cognition",
  url = "http://rpsonline.com.sg/rps2prod/iccd2015/pdf/I-002.pdf"
}

R. Viguier, C. C. Lin, K. Swaminathan, A. Vega, A. Buyuktosunoglu, S. Pankanti, P. Bose, H. Akbarpour, F. Bunyak, K. Palaniappan, and G. Seetharaman. Resilient mobile cognition: Algorithms, innovations, and architectures. IEEE International Conference on  Computer Design (ICCD), pages 4pp, October 2015.

#203: M. F. Pellechia, K. Palaniappan, P. J. Doucette, S. L. Dockstader, and G. S. (Editors)

Geospatial Informatics, Fusion and Motion Video Analytics VI

Proceedings of SPIE, Volume 9473, pgs. 256, 2015

Bibtex, PlainText, Google Scholar

@book{2015a,
  author = "M. F. Pellechia and K. Palaniappan and P. J. Doucette and S. L. Dockstader and G. S. (Editors)",
  title = "Geospatial Informatics, Fusion and Motion Video Analytics VI",
  year = 2015,
  journal = "Proceedings of SPIE",
  volume = 9473,
  pages = "256",
  month = "Jun"
}

#201: A. Haridas, F. Bunyak, and K. Palaniappan

Interactive segmentation relabeling for classification of whole-slide histopathology imagery

IEEE Int. Symposium on Computer-Based Medical Systems (CBMS), pgs. 84-87, 2015

visualization, big data, biomedical, histopathology

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Haridas:CBMS-2015,
  author = "A. Haridas and F. Bunyak and K. Palaniappan",
  title = "Interactive segmentation relabeling for classification of whole-slide histopathology imagery",
  year = 2015,
  booktitle = "IEEE Int. Symposium on Computer-Based Medical Systems (CBMS)",
  pages = "84-87",
  month = "Jun",
  keywords = "visualization, big data, biomedical, histopathology",
  doi = "10.1109/CBMS.2015.89",
  url = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7167462"
}

A. Haridas, F. Bunyak, and K. Palaniappan. Interactive segmentation relabeling for classification of whole-slide histopathology imagery. IEEE Int. Symposium on Computer-Based Medical Systems (CBMS), pages 84-87, June 2015.

#215: F. Bunyak, A. Hafiane, Z. Al-Milagi, I. Ersoy, A. Haridas, and K. Palaniappan

A segmentation-based multi-scale framework for the classification of epithelial and stromal tissues in H&E images

Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pgs. 450-453, 2015

biomedical, histopathology, segmentation, classification

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{Bunyak:histo-seg-2015,
  author = "F. Bunyak and A. Hafiane and Z. Al-Milagi and I. Ersoy and A. Haridas and K. Palaniappan",
  title = "A segmentation-based multi-scale framework for the classification of epithelial and stromal tissues in H&E images",
  year = 2015,
  booktitle = "Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
  pages = "450-453",
  keywords = "biomedical, histopathology, segmentation, classification",
  url = "http://cci.drexel.edu/ieeebibm/bibm2015/index.html"
}

F. Bunyak, A. Hafiane, Z. Al-Milagi, I. Ersoy, A. Haridas, and K. Palaniappan. A segmentation-based multi-scale framework for the classification of epithelial and stromal tissues in H&E images. Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 450-453, 2015.

#197: V. B. S. Prasath, D. Vorotnikov, R. Pelapur, S. Jose, G. Seetharaman, and K. Palaniappan

Multiscale Tikhonov-total variation image restoration using spatially varying edge coherence exponent

IEEE Transactions on Image Processing, Volume 24, pgs. 5220-5235, 2015

image restoration, regularization, anisotropic diffusion, total variation

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@article{Surya:MTTV-TIP-2015,
  author = "V. B. S. Prasath and D. Vorotnikov and R. Pelapur and S. Jose and G. Seetharaman and K. Palaniappan",
  title = "Multiscale Tikhonov-total variation image restoration using spatially varying edge coherence exponent",
  year = 2015,
  journal = "IEEE Transactions on Image Processing",
  volume = 24,
  number = 12,
  pages = "5220-5235",
  keywords = "image restoration, regularization, anisotropic diffusion, total variation",
  doi = "10.1109/TIP.2015.2479471",
  url = "http://cell.missouri.edu/pages/MTTV/"
}

V. B. S. Prasath, D. Vorotnikov, R. Pelapur, S. Jose, G. Seetharaman, and K. Palaniappan. Multiscale Tikhonov-total variation image restoration using spatially varying edge coherence exponent. IEEE Transactions on Image Processing, volume 24, issue 12, pages 5220-5235, 2015.

#206: S. Thutupalli, M. Sun, F. Bunyak, K. Palaniappan, and J. W. Shaevitz

Directional reversals enable Myxococcus xanthus cells to produce collective one-dimensional streams during fruiting-body formation

Journal of The Royal Society Interface, Volume 12, pgs. 20150049, 2015

biomedical, cell tracking, microscopy

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

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  author = "S. Thutupalli and M. Sun and F. Bunyak and K. Palaniappan and J. W. Shaevitz",
  title = "Directional reversals enable Myxococcus xanthus cells to produce collective one-dimensional streams during fruiting-body formation",
  year = 2015,
  journal = "Journal of The Royal Society Interface",
  volume = 12,
  number = 109,
  pages = "20150049",
  keywords = "biomedical, cell tracking, microscopy",
  doi = "10.1098/rsif.2015.0049",
  url = "http://arxiv.org/pdf/1410.7230"
}

S. Thutupalli, M. Sun, F. Bunyak, K. Palaniappan, and J. W. Shaevitz. Directional reversals enable Myxococcus xanthus cells to produce collective one-dimensional streams during fruiting-body formation. Journal of The Royal Society Interface, volume 12, issue 109, pages 20150049, 2015.

#200: A. Hafiane, K. Palaniappan, and G. Seetharaman

Joint Adaptive Median Binary Patterns for texture classification

Pattern Recognition, Volume 48, pgs. 2609-2620, 2015

texture, lbp, classification, cbir

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

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  author = "A. Hafiane and K. Palaniappan and G. Seetharaman",
  title = "Joint Adaptive Median Binary Patterns for texture classification",
  year = 2015,
  journal = "Pattern Recognition",
  volume = 48,
  number = 8,
  pages = "2609-2620",
  keywords = "texture, lbp, classification, cbir",
  doi = "10.1016/j.patcog.2015.02.007",
  url = "http://www.sciencedirect.com/science/article/pii/S0031320315000679"
}

A. Hafiane, K. Palaniappan, and G. Seetharaman. Joint Adaptive Median Binary Patterns for texture classification. Pattern Recognition, volume 48, issue 8, pages 2609-2620, 2015.

#198: V. B. S. Prasath, R. Pelapur, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, and K. Palaniappan

Multiscale tensor anisotropic filtering of fluorescence microscopy for denoising microvasculature

IEEE Int. Symposium on Biomedical Imaging (ISBI), pgs. 540-543, 2015

anisotropic diffusion, denoising, vasculature, biomedical

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

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  author = "V. B. S. Prasath and R. Pelapur and O. V. Glinskii and V. V. Glinsky and V. H. Huxley and K. Palaniappan",
  title = "Multiscale tensor anisotropic filtering of fluorescence microscopy for denoising microvasculature",
  year = 2015,
  booktitle = "IEEE Int. Symposium on Biomedical Imaging (ISBI)",
  pages = "540-543",
  keywords = "anisotropic diffusion, denoising, vasculature, biomedical",
  doi = "10.1109/ISBI.2015.7163930",
  url = "https://sites.google.com/site/suryaiit/research/microscopy/denoise"
}

V. B. S. Prasath, R. Pelapur, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, and K. Palaniappan. Multiscale tensor anisotropic filtering of fluorescence microscopy for denoising microvasculature. IEEE Int. Symposium on Biomedical Imaging (ISBI), pages 540-543, 2015.

#191: A. Hafiane, K. Palaniappan, and G. Seetharaman

Adaptive median binary patterns for texture classification

Proc. IEEE International Conference on Pattern Recognition (ICPR), 2014

texture, lbp, classification, cbir

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

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  author = "A. Hafiane and K. Palaniappan and G. Seetharaman",
  title = "Adaptive median binary patterns for texture classification ",
  year = 2014,
  booktitle = "Proc. IEEE International Conference on Pattern Recognition (ICPR)",
  month = "Aug",
  keywords = "texture, lbp, classification, cbir",
  doi = "10.1109/ICPR.2014.205"
}

A. Hafiane, K. Palaniappan, and G. Seetharaman. Adaptive median binary patterns for texture classification . Proc. IEEE International Conference on Pattern Recognition (ICPR), August 2014.

#187: J. C. Moreno, V. B. S. Prasath, H. Proenca, and K. Palaniappan

Fast and globally convex multiphase active contours for brain MRI segmentation

Computer Vision and Image Understanding, Volume 125, pgs. 237-250, 2014

segmentation, active contours, globally convex, biomedical

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@article{Juan:BrainMRI-Segmentation-CVIU-2014,
  author = "J. C. Moreno and V. B. S. Prasath and H. Proenca and K. Palaniappan",
  title = "Fast and globally convex multiphase active contours for brain MRI segmentation",
  year = 2014,
  journal = "Computer Vision and Image Understanding",
  volume = 125,
  pages = "237-250",
  month = "Aug",
  keywords = "segmentation, active contours, globally convex, biomedical",
  doi = "10.1016/j.cviu.2014.04.010",
  url = "http://cell.missouri.edu/pages/BrainMRISegmentation/"
}

J. C. Moreno, V. B. S. Prasath, H. Proenca, and K. Palaniappan. Fast and globally convex multiphase active contours for brain MRI segmentation. Computer Vision and Image Understanding, volume 125, pages 237-250, August 2014.

#185: M. F. Pellechia, K. Palaniappan, S. L. Dockstader, P. B. Deignan, P. J. Doucette, and D. S. (Editors)

Geospatial InfoFusion and Video Analytics IV

Proceedings of SPIE, Volume 9089, 2014

Bibtex, PlainText, DOI, Google Scholar

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  title = "Geospatial InfoFusion and Video Analytics IV",
  year = 2014,
  journal = "Proceedings of SPIE",
  volume = 9089,
  month = "Jul",
  doi = "doi:10.1117/12.2072312"
}

#179: M. Sun, J. Huang, F. Bunyak, K. Gumpper, G. De, M. Sermersheim, G. Liu, P.-H. Lin, K. Palaniappan, and J. Ma

Superresolution microscope image reconstruction by spatiotemporal object decomposition and association: application in resolving t-tubule structure in skeletal muscle

Optics Express, Volume 22, pgs. 12160-12176, 2014

biomedical, image analysis

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

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  author = "M. Sun and J. Huang and F. Bunyak and K. Gumpper and G. De and M. Sermersheim and G. Liu and P.-H. Lin and K. Palaniappan and J. Ma",
  title = "Superresolution microscope image reconstruction by spatiotemporal object decomposition and association: application in resolving t-tubule structure in skeletal muscle",
  year = 2014,
  journal = "Optics Express",
  volume = 22,
  number = 10,
  pages = "12160-12176",
  month = "May",
  keywords = "biomedical, image analysis",
  doi = "10.1364/OE.22.012160",
  url = "http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-22-10-12160"
}

M. Sun, J. Huang, F. Bunyak, K. Gumpper, G. De, M. Sermersheim, G. Liu, P.-H. Lin, K. Palaniappan, and J. Ma. Superresolution microscope image reconstruction by spatiotemporal object decomposition and association: application in resolving t-tubule structure in skeletal muscle. Optics Express, volume 22, issue 10, pages 12160-12176, May 2014.

#177: Z. Hong, Z. Sun, M. Li, Z. Li, F. Bunyak, I. Ersoy, J. P. Trzeciakowski, M. C. Staiculescu, M. Jin, L. Martinez-Lemus, M. A. Hill, K. Palaniappan, and G. A. Meininger

Vasoactive agonists exert dynamic and coordinated effects on vascular smooth muscle cell elasticity, cytoskeletal remodelling and adhesion

The Journal of Physiology, Volume 592, pgs. 1249-1266, 2014

biomedical, vascular

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@article{Hong:Vasoactive-Physiology-2014a,
  author = "Z. Hong and Z. Sun and M. Li and Z. Li and F. Bunyak and I. Ersoy and J. P. Trzeciakowski and M. C. Staiculescu and M. Jin and L. Martinez-Lemus and M. A. Hill and K. Palaniappan and G. A. Meininger",
  title = "Vasoactive agonists exert dynamic and coordinated effects on vascular smooth muscle cell elasticity, cytoskeletal remodelling and adhesion",
  year = 2014,
  journal = "The Journal of Physiology",
  volume = 592,
  pages = "1249-1266",
  month = "Mar",
  keywords = "biomedical, vascular",
  doi = "10.1113/jphysiol.2013.264929",
  url = "http://jp.physoc.org/content/592/6/1249.short"
}

Z. Hong, Z. Sun, M. Li, Z. Li, F. Bunyak, I. Ersoy, J. P. Trzeciakowski, M. C. Staiculescu, M. Jin, L. Martinez-Lemus, M. A. Hill, K. Palaniappan, and G. A. Meininger. Vasoactive agonists exert dynamic and coordinated effects on vascular smooth muscle cell elasticity, cytoskeletal remodelling and adhesion. The Journal of Physiology, volume 592, pages 1249-1266, March 2014.

#178: A. Gning, W. T. L. Teacy, R. Pelapur, H. AliAkbarpour, K. Palaniappan, G. Seetharaman, and S. J. Julier

The effect of state dependent probability of detection in multitarget tracking applications

Proc. SPIE Conf. Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II (Defense, Security and Sensing), Volume 9089, 2014

tracking, multitarget, dod

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Gning:Multitarget-SPIE-2014,
  author = "A. Gning and W. T. L. Teacy and R. Pelapur and H. AliAkbarpour and K. Palaniappan and G. Seetharaman and S. J. Julier",
  title = "The effect of state dependent probability of detection in multitarget tracking applications",
  year = 2014,
  booktitle = "Proc. SPIE Conf. Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II (Defense, Security and Sensing)",
  volume = 9089,
  keywords = "tracking, multitarget, dod",
  doi = "10.1117/12.2053968"
}

A. Gning, W. T. L. Teacy, R. Pelapur, H. AliAkbarpour, K. Palaniappan, G. Seetharaman, and S. J. Julier. The effect of state dependent probability of detection in multitarget tracking applications. Proc. SPIE Conf. Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II (Defense, Security and Sensing), volume 9089, 2014.

#188: S. Meena, V. B. S. Prasath, K. Palaniappan, and G. Seetharaman

Elastic body spline based image segmentation

Proc. IEEE International Conference on Image Processing (ICIP), pgs. 4378-4382, 2014

segmentation, interactive, elastic body, splines

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Sachin:Interactiveseg-ICIP-2014,
  author = "S. Meena and V. B. S. Prasath and K. Palaniappan and G. Seetharaman",
  title = "Elastic body spline based image segmentation",
  year = 2014,
  booktitle = "Proc. IEEE International Conference on Image Processing (ICIP)",
  pages = "4378-4382",
  keywords = "segmentation, interactive, elastic body, splines",
  doi = "10.1109/ICIP.2014.7025888"
}

S. Meena, V. B. S. Prasath, K. Palaniappan, and G. Seetharaman. Elastic body spline based image segmentation. Proc. IEEE International Conference on Image Processing (ICIP), pages 4378-4382, 2014.

#186: V. B. S. Prasath, R. Pelapur, K. Palaniappan, and G. Seetharaman

Feature fusion and label propagation for textured object video segmentation

Proc. SPIE Conf. Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II (Defense, Security and Sensing), Volume 9089, 2014

segmentation, active contours, video, globally convex

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Surya:Videoseg-SPIE-2014,
  author = "V. B. S. Prasath and R. Pelapur and K. Palaniappan and G. Seetharaman",
  title = "Feature fusion and label propagation for textured object video segmentation",
  year = 2014,
  booktitle = "Proc. SPIE Conf.  Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II (Defense, Security and Sensing)",
  volume = 9089,
  keywords = "segmentation, active contours, video, globally convex",
  doi = "10.1117/12.2052983"
}

V. B. S. Prasath, R. Pelapur, K. Palaniappan, and G. Seetharaman. Feature fusion and label propagation for textured object video segmentation. Proc. SPIE Conf.  Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II (Defense, Security and Sensing), volume 9089, 2014.

#183: R. Pelapur, V. B. S. Prasath, F. Bunyak, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, and K. Palaniappan

Multi-focus image fusion using epifluorescence microscopy for robust vascular segmentation

36th IEEE Engineering in Medicine and Biology Society Conf. (EMBC), pgs. 4735-4738, 2014

multifocus, image fusion, vasculature, biomedical

Abstract, Bibtex, PlainText, PDF, PubMed, DOI, Google Scholar

@inproceedings{Rengarajan:Multifocus-EMBS-2014,
  author = "R. Pelapur and V. B. S. Prasath and F. Bunyak and O. V. Glinskii and V. V. Glinsky and V. H. Huxley and K. Palaniappan",
  title = "Multi-focus image fusion using epifluorescence microscopy for robust vascular segmentation",
  year = 2014,
  booktitle = "36th IEEE Engineering in Medicine and Biology Society Conf. (EMBC)",
  pages = "4735-4738",
  keywords = "multifocus, image fusion, vasculature, biomedical",
  doi = "10.1109/EMBC.2014.6944682",
  url = "http://www.ncbi.nlm.nih.gov/pubmed/25571050"
}

R. Pelapur, V. B. S. Prasath, F. Bunyak, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, and K. Palaniappan. Multi-focus image fusion using epifluorescence microscopy for robust vascular segmentation. 36th IEEE Engineering in Medicine and Biology Society Conf. (EMBC), pages 4735-4738, 2014.

#180: R. Wang, F. Bunyak, G. Seetharaman, and K. Palaniappan

Static and moving object detection using flux tensor with split Gaussian models

Proc. of IEEE CVPR Workshop on Change Detection, 2014

ftsg, flux tensor, split gaussian, change detection

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Rui:FTSG-ChangeDetection-CVPRW-2014,
  author = "R. Wang and F. Bunyak and G. Seetharaman and K. Palaniappan",
  title = "Static and moving object detection using flux tensor with split Gaussian models",
  year = 2014,
  booktitle = "Proc. of IEEE CVPR Workshop on Change Detection",
  keywords = "ftsg, flux tensor, split gaussian, change detection",
  doi = "10.1109/CVPRW.2014.68",
  url = "http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2014/W12/papers/Wang_Static_and_Moving_2014_CVPR_paper.pdf"
}

R. Wang, F. Bunyak, G. Seetharaman, and K. Palaniappan. Static and moving object detection using flux tensor with split Gaussian models. Proc. of IEEE CVPR Workshop on Change Detection, 2014.

#184: S. Candemir, S. Jaeger, K. Palaniappan, J. P. Musco, R. Singh, Z. Xue, A. Karargyris, S. Antani, G. Thoma, and C. J. McDonald

Lung segmentation in chest radiographs using anatomical atlases with non-rigid registration

IEEE Trans. Medical Imaging, Volume 33, pgs. 577-590, 2014

segmentation, graph methods, chest x-ray, cbir, registration, biomedical

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@article{Sema:TMI-2013-lung-seg,
  author = "S. Candemir and S. Jaeger and K. Palaniappan and J. P. Musco and R. Singh and Z. Xue and A. Karargyris and S. Antani and G. Thoma and C. J. McDonald",
  title = "Lung segmentation in chest radiographs using anatomical atlases with non-rigid registration",
  year = 2014,
  journal = "IEEE Trans. Medical Imaging",
  volume = 33,
  number = 2,
  pages = "577-590",
  keywords = "segmentation, graph methods, chest x-ray, cbir, registration, biomedical",
  doi = "10.1109/TMI.2013.2290491"
}

S. Candemir, S. Jaeger, K. Palaniappan, J. P. Musco, R. Singh, Z. Xue, A. Karargyris, S. Antani, G. Thoma, and C. J. McDonald. Lung segmentation in chest radiographs using anatomical atlases with non-rigid registration. IEEE Trans. Medical Imaging, volume 33, issue 2, pages 577-590, 2014.

#189: S. Jaeger, A. Karargyris, S. Candemir, L. Folio, J. Siegelman, F. Callaghan, Z. Xue, K. Palaniappan, R. Singh, S. Antani, G. Thoma, Y.-X. Wang, P.-X. Lu, and C. J. McDonald

Automatic tuberculosis screening using chest radiographs

IEEE Trans. Medical Imaging, Volume 33, pgs. 233-245, 2014

classification, features, machine learning, data mining, biomedical

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@article{Jaeger:TMI-2013-TBScreening,
  author = "S. Jaeger and A. Karargyris and S. Candemir and L. Folio and J. Siegelman and F. Callaghan and Z. Xue and K. Palaniappan and R. Singh and S. Antani and G. Thoma and Y.-X. Wang and P.-X. Lu and C. J. McDonald",
  title = "Automatic tuberculosis screening using chest radiographs",
  year = 2014,
  journal = "IEEE Trans. Medical Imaging",
  volume = 33,
  number = 2,
  pages = "233-245",
  keywords = "classification, features, machine learning, data mining, biomedical",
  doi = "10.1109/TMI.2013.2284099"
}

S. Jaeger, A. Karargyris, S. Candemir, L. Folio, J. Siegelman, F. Callaghan, Z. Xue, K. Palaniappan, R. Singh, S. Antani, G. Thoma, Y.-X. Wang, P.-X. Lu, and C. J. McDonald. Automatic tuberculosis screening using chest radiographs. IEEE Trans. Medical Imaging, volume 33, issue 2, pages 233-245, 2014.

#167: S. Candemir, K. Palaniappan, and Y. S. Akgul

Multi-class regularization parameter learning for graph cut image segmentation

IEEE Int. Symposium on Biomedical Imaging (ISBI), 2013

segmentation, graph methods, chest x-ray, machine learning, biomedical

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Sema:ISBI-2013-graph-cut,
  author = "S. Candemir and K. Palaniappan and Y. S. Akgul",
  title = "Multi-class regularization parameter learning for graph cut image segmentation",
  year = 2013,
  booktitle = "IEEE Int. Symposium on Biomedical Imaging (ISBI)",
  keywords = "segmentation, graph methods, chest x-ray, machine learning, biomedical",
  doi = "10.1109/ISBI.2013.6556813"
}

S. Candemir, K. Palaniappan, and Y. S. Akgul. Multi-class regularization parameter learning for graph cut image segmentation. IEEE Int. Symposium on Biomedical Imaging (ISBI), 2013.

#172: H. Aliakbarpour, K. Palaniappan, and J. Dias

Geometric exploration of virtual planes in a fusion-based 3D registration framework

Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation), Volume 8747, pgs. 87470C, 2013

3d reconstruction, wami, registration, fusion, fmv, dod

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Hadi:virtual-planes_SPIE-2013,
  author = "H. Aliakbarpour and K. Palaniappan and J. Dias",
  title = "Geometric exploration of virtual planes in a fusion-based 3D registration framework",
  year = 2013,
  booktitle = "Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation)",
  volume = 8747,
  pages = "87470C",
  keywords = "3d reconstruction, wami, registration, fusion, fmv, dod",
  doi = "10.1117/12.2015933"
}

H. Aliakbarpour, K. Palaniappan, and J. Dias. Geometric exploration of virtual planes in a fusion-based 3D registration framework. Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation), volume 8747, pages 87470C, 2013.

#166: V. B. S. Prasath, O. Haddad, F. Bunyak, O. Glinskii, V. Glinsky, V. Huxley, and K. Palaniappan

Robust filtering based segmentation and analysis of dura mater vessel laminae using epiflourescence microscopy

35th IEEE Engineering in Medicine and Biology Society Conf. (EMBC), pgs. 6055-6058, 2013

segmentation, image enhancement, vasculature, biomedical

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{Surya:EMBC-2013-vessel,
  author = "V. B. S. Prasath and O. Haddad and F. Bunyak and O. Glinskii and V. Glinsky and V. Huxley and K. Palaniappan",
  title = "Robust filtering based segmentation and analysis of dura mater vessel laminae using epiflourescence microscopy",
  year = 2013,
  booktitle = "35th IEEE Engineering in Medicine and Biology Society Conf. (EMBC)",
  pages = "6055-6058",
  keywords = "segmentation, image enhancement, vasculature, biomedical"
}

V. B. S. Prasath, O. Haddad, F. Bunyak, O. Glinskii, V. Glinsky, V. Huxley, and K. Palaniappan. Robust filtering based segmentation and analysis of dura mater vessel laminae using epiflourescence microscopy. 35th IEEE Engineering in Medicine and Biology Society Conf. (EMBC), pages 6055-6058, 2013.

#169: Z. Hong, I. Ersoy, M. Sun, F. Bunyak, P. Hampel, Z. Hong, Z. Sun, Z. Li, I. Levitan, G. A. Meininger, and K. Palaniappan

Influence of membrane cholesterol and substrate elasticity on endothelial cell spreading behavior

J. Biomedical Material Research Part A, 2013

cell segmentation, active contours, high content, biomedical

Abstract, Bibtex, PlainText, PDF, PubMed, DOI, Google Scholar

@article{Zhongqui:cell-spreading-2012,
  author = "Z. Hong and I. Ersoy and M. Sun and F. Bunyak and P. Hampel and Z. Hong and Z. Sun and Z. Li and I. Levitan and G. A. Meininger and K. Palaniappan",
  title = "Influence of membrane cholesterol and substrate elasticity on endothelial cell spreading behavior",
  year = 2013,
  journal = "J. Biomedical Material Research Part A",
  keywords = "cell segmentation, active contours, high content, biomedical",
  doi = "10.1002/jbm.a.34504",
  url = "http://www.ncbi.nlm.nih.gov/pubmed/23239612"
}

Z. Hong, I. Ersoy, M. Sun, F. Bunyak, P. Hampel, Z. Hong, Z. Sun, Z. Li, I. Levitan, G. A. Meininger, and K. Palaniappan. Influence of membrane cholesterol and substrate elasticity on endothelial cell spreading behavior. J. Biomedical Material Research Part A, 2013.

#164: R. Pelapur, F. Bunyak, G. Seetharaman, and K. Palaniappan

Vehicle detection and orientation estimation using the Radon transform

Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation), Volume 8747, pgs. 87470I, 2013

wami, tracking, fmv, motion, dod

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Rengarajan:Orientation-estim_SPIE-2013,
  author = "R. Pelapur and F. Bunyak and G. Seetharaman and K. Palaniappan",
  title = "Vehicle detection and orientation estimation using the Radon transform",
  year = 2013,
  booktitle = "Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation)",
  volume = 8747,
  pages = "87470I",
  keywords = "wami, tracking, fmv, motion, dod",
  doi = "10.1117/12.2016407"
}

R. Pelapur, F. Bunyak, G. Seetharaman, and K. Palaniappan. Vehicle detection and orientation estimation using the Radon transform. Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation), volume 8747, pages 87470I, 2013.

#173: M. Poostchi, F. Bunyak, and K. Palaniappan

Feature selection for appearance-based vehicle tracking in geospatial video

Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation), Volume 8747, pgs. 87470G, 2013

wami, tracking, fmv, features, motion, dod

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Mahdieh:FeatureSelection_SPIE-2013,
  author = "M. Poostchi and F. Bunyak and K. Palaniappan",
  title = "Feature selection for appearance-based vehicle tracking in geospatial video",
  year = 2013,
  booktitle = "Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation)",
  volume = 8747,
  pages = "87470G",
  keywords = "wami, tracking, fmv, features, motion, dod",
  doi = "10.1117/12.2015672"
}

M. Poostchi, F. Bunyak, and K. Palaniappan. Feature selection for appearance-based vehicle tracking in geospatial video. Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation), volume 8747, pages 87470G, 2013.

#168: J. Fraser, A. Haridas, G. Seetharaman, R. Rao, and K. Palaniappan

KOLAM: A cross-platform architecture for scalable visualization and tracking in wide-area motion imagery

Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation), Volume 8747, pgs. 87470N, 2013

visualization, big data, wami, tracking, fmv, features, motion, dod, biomedical

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

KOLAM is an open, cross-platform, interoperable, scalable and extensible framework supporting a novel multi- scale spatiotemporal dual-cache data structure for big data visualization and visual analytics. This paper focuses on the use of KOLAM for target tracking in high-resolution, high throughput wide format video also known as wide-area motion imagery (WAMI). It was originally developed for the interactive visualization of extremely large geospatial imagery of high spatial and spectral resolution. KOLAM is platform, operating system and (graphics) hardware independent, and supports embedded datasets scalable from hundreds of gigabytes to feasibly petabytes in size on clusters, workstations, desktops and mobile computers. In addition to rapid roam, zoom and hyper- jump spatial operations, a large number of simultaneously viewable embedded pyramid layers (also referred to as multiscale or sparse imagery), interactive colormap and histogram enhancement, spherical projection and terrain maps are supported. The KOLAM software architecture was extended to support airborne wide-area motion imagery by organizing spatiotemporal tiles in very large format video frames using a temporal cache of tiled pyramid cached data structures. The current version supports WAMI animation, fast intelligent inspection, trajectory visualization and target tracking (digital tagging); the latter by interfacing with external automatic tracking software. One of the critical needs for working with WAMI is a supervised tracking and visualization tool that allows analysts to digitally tag multiple targets, quickly review and correct tracking results and apply geospatial visual analytic tools on the generated trajectories. One-click manual tracking combined with multiple automated tracking algorithms are available to assist the analyst and increase human effectiveness. Keywords: wide-area motion imagery, WAMI, digital tagging, assisted tracking, trajectory visualization, tra- jectory data mining, spatiotemporal cache, multiresolution tiled pyramid imagery

@inproceedings{Fraser:KOLAM_SPIE-2013,
  author = "J. Fraser and A. Haridas and G. Seetharaman and R. Rao and K. Palaniappan",
  title = "KOLAM: A cross-platform architecture for scalable visualization and tracking in wide-area motion imagery",
  year = 2013,
  booktitle = "Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation)",
  volume = 8747,
  pages = "87470N",
  keywords = "visualization, big data, wami, tracking, fmv, features, motion, dod, biomedical",
  doi = "10.1117/12.2018162"
}

J. Fraser, A. Haridas, G. Seetharaman, R. Rao, and K. Palaniappan. KOLAM: A cross-platform architecture for scalable visualization and tracking in wide-area motion imagery. Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation), volume 8747, pages 87470N, 2013.

#157: M. F. Pellechia, R. J. Sorensen, S. L. Dockstader, K. Palaniappan, X. L. (Editors), and

Geospatial InfoFusion III

Proceedings of SPIE, Volume 8396, pgs. 204, 2012

Bibtex, PlainText, ISBN: 9780819490742, Google Scholar

@book{2012a,
  author = "M. F. Pellechia and R. J. Sorensen and S. L. Dockstader and K. Palaniappan and X. L. (Editors) and ",
  title = "Geospatial InfoFusion III",
  year = 2012,
  journal = "Proceedings of SPIE",
  volume = 8396,
  pages = "204",
  month = "Jun",
  note = "ISBN: 9780819490742"
}

#140: E. Blasch, G. Seetharaman, K. Palaniappan, H. Ling, and G. Chen

Wide-Area Motion Imagery (WAMI) exploitation tools for enhanced situational awareness

Proc. IEEE Applied Imagery Pattern Recognition Workshop, 2012

wami, visual events, fusion, data mining, fmv, dod

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{Blasch:wami-tools-2012,
  author = "E. Blasch and G. Seetharaman and K. Palaniappan and H. Ling and G. Chen",
  title = "Wide-Area Motion Imagery (WAMI) exploitation tools for enhanced situational awareness",
  year = 2012,
  booktitle = "Proc. IEEE Applied Imagery Pattern Recognition Workshop",
  keywords = "wami, visual events, fusion, data mining, fmv, dod",
  url = "http://www.dabi.temple.edu/~hbling/publication/AIPR12_WAMI_for_E2ATv4_BW.pdf"
}

E. Blasch, G. Seetharaman, K. Palaniappan, H. Ling, and G. Chen. Wide-Area Motion Imagery (WAMI) exploitation tools for enhanced situational awareness. Proc. IEEE Applied Imagery Pattern Recognition Workshop, 2012.

#148: A. Camargo, Q. He, and K. Palaniappan

Performance evaluation of optimization methods for super-resolution mosaicking on UAS surveillance videos

Proc. SPIE Infrared Imaging Systems: Design, Analysis, Modeling and Testing XXIII, Volume 8355, 2012

image enhancement, wami, visual events, fusion, fmv, dod

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Camargo:super-resolution_SPIE-2012,
  author = "A. Camargo and Q. He and K. Palaniappan",
  title = "Performance evaluation of optimization methods for super-resolution mosaicking on UAS surveillance videos",
  year = 2012,
  booktitle = "Proc. SPIE Infrared Imaging Systems: Design, Analysis, Modeling and Testing XXIII",
  volume = 8355,
  keywords = "image enhancement, wami, visual events, fusion, fmv, dod",
  doi = "10.1117/12.919641",
  url = "http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1385560"
}

A. Camargo, Q. He, and K. Palaniappan. Performance evaluation of optimization methods for super-resolution mosaicking on UAS surveillance videos. Proc. SPIE Infrared Imaging Systems: Design, Analysis, Modeling and Testing XXIII, volume 8355, 2012.

#152: V. B. S. Prasath, I. Figueiredo, P. Figueiredo, and K. Palaniappan

Mucosal region detection and 3D reconstruction in wireless capsule endoscopy videos using active contours

34th IEEE Engineering in Medicine and Biology Society Conf. (EMBC), pgs. 4014--4017, 2012

cell detection, cell segmentation, cell tracking, active contours, red blood cells, biomedical

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Surya:EMBC-2012-wce,
  author = "V. B. S. Prasath and I. Figueiredo and P. Figueiredo and K. Palaniappan",
  title = "Mucosal region detection and 3D reconstruction in wireless capsule endoscopy videos using active contours",
  year = 2012,
  booktitle = "34th IEEE Engineering in Medicine and Biology Society Conf. (EMBC)",
  pages = "4014--4017",
  keywords = "cell detection, cell segmentation, cell tracking, active contours, red blood cells, biomedical",
  doi = "10.1109/EMBC.2012.6346847"
}

V. B. S. Prasath, I. Figueiredo, P. Figueiredo, and K. Palaniappan. Mucosal region detection and 3D reconstruction in wireless capsule endoscopy videos using active contours. 34th IEEE Engineering in Medicine and Biology Society Conf. (EMBC), pages 4014--4017, 2012.

#160: M. Poostchi, K. Palaniappan, F. Bunyak, M. Becchi, and G. Seetharaman

Efficient GPU implementation of the integral histogram

Lecture Notes in Computer Science (ACCV Workshop on Developer-Centered Computer Vision), Volume 7728, pgs. 266--278, 2012

parallelization, gpu, tracking, fmv, motion, features, dod

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{Mahdieh:ACCVW-2012-gpuIH,
  author = "M. Poostchi and K. Palaniappan and F. Bunyak and M. Becchi and G. Seetharaman",
  title = "Efficient GPU implementation of the integral histogram",
  year = 2012,
  booktitle = "Lecture Notes in Computer Science (ACCV Workshop on Developer-Centered Computer Vision)",
  volume = 7728,
  number = Part I,
  pages = "266--278",
  keywords = "parallelization, gpu, tracking, fmv, motion, features, dod"
}

M. Poostchi, K. Palaniappan, F. Bunyak, M. Becchi, and G. Seetharaman. Efficient GPU implementation of the integral histogram. Lecture Notes in Computer Science (ACCV Workshop on Developer-Centered Computer Vision), volume 7728, issue Part I, pages 266--278, 2012.

#154: I. Ersoy, K. Palaniappan, G. Seetharaman, and R. Rao

Interactive tracking for persistent wide-area surveillance

Proc. SPIE Conf. Geospatial InfoFusion II (Defense, Security and Sensing: Sensor Data and Information Exploitation), Volume 8396, 2012

wami, tracking, fmv, features, motion, fusion, dod

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{Ersoy:tracking_SPIE-2012,
  author = "I. Ersoy and K. Palaniappan and G. Seetharaman and R. Rao",
  title = "Interactive tracking for persistent wide-area surveillance",
  year = 2012,
  booktitle = "Proc. SPIE Conf. Geospatial InfoFusion II (Defense, Security and Sensing: Sensor Data and Information Exploitation)",
  volume = 8396,
  keywords = "wami, tracking, fmv, features, motion, fusion, dod"
}

I. Ersoy, K. Palaniappan, G. Seetharaman, and R. Rao. Interactive tracking for persistent wide-area surveillance. Proc. SPIE Conf. Geospatial InfoFusion II (Defense, Security and Sensing: Sensor Data and Information Exploitation), volume 8396, 2012.

#161: I. Ersoy, F. Bunyak, J. Higgins, and K. Palaniappan

Coupled edge profile geodesic active contours for red blood cell flow analysis

IEEE Int. Symposium on Biomedical Imaging (ISBI), 2012

cell segmentation, level set active contours, red blood cells, cell tracking, biomedical

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Ersoy:ISBI-2012-rbc,
  author = "I. Ersoy and F. Bunyak and J. Higgins and K. Palaniappan",
  title = "Coupled edge profile geodesic active contours for red blood cell flow analysis",
  year = 2012,
  booktitle = "IEEE Int. Symposium on Biomedical Imaging (ISBI)",
  keywords = "cell segmentation, level set active contours, red blood cells, cell tracking, biomedical",
  doi = "10.1109/ISBI.2012.6235656",
  url = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6235656"
}

I. Ersoy, F. Bunyak, J. Higgins, and K. Palaniappan. Coupled edge profile geodesic active contours for red blood cell flow analysis. IEEE Int. Symposium on Biomedical Imaging (ISBI), 2012.

#159: S. Candemir, K. Palaniappan, F. Bunyak, and G. Seetharaman

Feature fusion using ranking for object tracking in aerial imagery

Proc. SPIE Conf. Geospatial InfoFusion II (Defense, Security and Sensing: Sensor Data and Information Exploitation), Volume 8396, 2012

wami, tracking, fmv, features, motion, fusion, dod

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

Aerial wide-area monitoring and tracking using multi-camera arrays poses unique challenges compared to stan- dard full motion video analysis due to low frame rate sampling, accurate registration due to platform motion, low resolution targets, limited image contrast, static and dynamic parallax occlusions.1–3 We have developed a low frame rate tracking system that fuses a rich set of intensity, texture and shape features, which enables adaptation of the tracker to dynamic environment changes and target appearance variabilities. However, improper fusion and overweighting of low quality features can adversely affect target localization and reduce tracking performance. Moreover, the large computational cost associated with extracting a large number of image-based feature sets will influence tradeoffs for real-time and on-board tracking. This paper presents a framework for dynamic online ranking-based feature evaluation and fusion in aerial wide-area tracking. We describe a set of efficient descriptors suitable for small sized targets in aerial video based on intensity, texture, and shape feature representations or views. Feature ranking is then used as a selection procedure where target-background discrimination power for each (raw) feature view is scored using a two-class variance ratio approach. A subset of the k-best discriminative features are selected for further processing and fusion. The target match probability or likelihood maps for each of the k features are estimated by comparing target descriptors within a search region using a sliding win- dow approach. The resulting k likelihood maps are fused for target localization using the normalized variance ratio weights. We quantitatively measure the performance of the proposed system using ground-truth tracks within the framework of our tracking evaluation test-bed that incorporates various performance metrics. The proposed feature ranking and fusion approach increases localization accuracy by reducing multimodal effects due to low quality features or background clutter. Adaptive feature ranking increases the robustness of the tracker in dynamically changing environments especially when the object appearance is changing.

@inproceedings{Sema:Feature-ranking_SPIE-2012,
  author = "S. Candemir and K. Palaniappan and F. Bunyak and G. Seetharaman",
  title = "Feature fusion using ranking for object tracking in aerial imagery",
  year = 2012,
  booktitle = "Proc. SPIE Conf. Geospatial InfoFusion II (Defense, Security and Sensing: Sensor Data and Information Exploitation)",
  volume = 8396,
  keywords = "wami, tracking, fmv, features, motion, fusion, dod",
  doi = "10.1117/12.920529"
}

S. Candemir, K. Palaniappan, F. Bunyak, and G. Seetharaman. Feature fusion using ranking for object tracking in aerial imagery. Proc. SPIE Conf. Geospatial InfoFusion II (Defense, Security and Sensing: Sensor Data and Information Exploitation), volume 8396, 2012.

#141: I. Ersoy, K. Palaniappan, and G. Seetharaman

Visual tracking with robust target localization

IEEE Int. Conf. Image Processing, pgs. 1365--1368, 2012

wami, tracking, fmv, features, motion, dod

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{Ersoy:csurf_ICIP-2012-csurf,
  author = "I. Ersoy and K. Palaniappan and G. Seetharaman",
  title = "Visual tracking with robust target localization",
  year = 2012,
  booktitle = "IEEE Int. Conf. Image Processing",
  pages = "1365--1368",
  keywords = "wami, tracking, fmv, features, motion, dod"
}

I. Ersoy, K. Palaniappan, and G. Seetharaman. Visual tracking with robust target localization. IEEE Int. Conf. Image Processing, pages 1365--1368, 2012.

#143: A. Chowdhury, A. Paul, F. Bunyak, D. Cornelison, and K. Palaniappan

Semi-automated tracking of muscle satellite cells in brightfield microscopy video

IEEE Int. Conf. Image Processing, 2012

cell detection, cell tracking, particle filters, image enhancement, biomedical

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Ananda:satellite-cell_ICIP-2012,
  author = "A. Chowdhury and A. Paul and F. Bunyak and D. Cornelison and K. Palaniappan",
  title = "Semi-automated tracking of muscle satellite cells in brightfield microscopy video",
  year = 2012,
  booktitle = "IEEE Int. Conf. Image Processing",
  keywords = "cell detection, cell tracking, particle filters, image enhancement, biomedical",
  doi = "10.1109/ICIP.2012.6467487"
}

A. Chowdhury, A. Paul, F. Bunyak, D. Cornelison, and K. Palaniappan. Semi-automated tracking of muscle satellite cells in brightfield microscopy video. IEEE Int. Conf. Image Processing, 2012.

#145: R. Pelapur, K. Palaniappan, and G. Seetharaman

Robust orientation and appearance adaptation for wide-area large format video object tracking

9th IEEE Int. Conf. Advanced Video and Signal-Based Surveillance, 2012

wami, tracking, fmv, motion, features, dod

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Rengarajan:Orientation-tracking_AVSS-2012,
  author = "R. Pelapur and K. Palaniappan and G. Seetharaman",
  title = "Robust orientation and appearance adaptation for wide-area large format video object tracking",
  year = 2012,
  booktitle = "9th IEEE Int. Conf. Advanced Video and Signal-Based Surveillance",
  keywords = "wami, tracking, fmv, motion, features, dod",
  doi = "10.1109/AVSS.2012.92"
}

R. Pelapur, K. Palaniappan, and G. Seetharaman. Robust orientation and appearance adaptation for wide-area large format video object tracking. 9th IEEE Int. Conf. Advanced Video and Signal-Based Surveillance, 2012.

#155: I. Ersoy, F. Bunyak, K. Palaniappan, and J. Peng

HEp-2 Cell Classification in IIF Images Using ShareBoost

Int. Conf. Pattern Recognition, 2012

cell detection, cell classification, features, texture, machine learning, data mining, biomedical

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{Ersoy:ICPR-2012-hep2,
  author = "I. Ersoy and F. Bunyak and K. Palaniappan and J. Peng",
  title = "HEp-2 Cell Classification in IIF Images Using ShareBoost",
  year = 2012,
  booktitle = "Int. Conf. Pattern Recognition",
  keywords = "cell detection, cell classification, features, texture, machine learning, data mining, biomedical",
  url = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6460885"
}

I. Ersoy, F. Bunyak, K. Palaniappan, and J. Peng. HEp-2 Cell Classification in IIF Images Using ShareBoost. Int. Conf. Pattern Recognition, 2012.

#153: J. Peng, K. Palaniappan, S. Candemir, and G. Seetharaman

Kullback-Leibler divergence-based data fusion for target tracking

Int. Conf. Pattern Recognition, 2012

wami, tracking, fmv, features, motion, fusion, data mining, machine learning, dod

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{JPeng:ICPR-2012,
  author = "J. Peng and K. Palaniappan and S. Candemir and G. Seetharaman",
  title = "Kullback-Leibler divergence-based data fusion for target tracking",
  year = 2012,
  booktitle = "Int. Conf. Pattern Recognition",
  keywords = "wami, tracking, fmv, features, motion, fusion, data mining, machine learning, dod",
  url = "http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6460914"
}

J. Peng, K. Palaniappan, S. Candemir, and G. Seetharaman. Kullback-Leibler divergence-based data fusion for target tracking. Int. Conf. Pattern Recognition, 2012.

#147: R. Pelapur, S. Candemir, F. Bunyak, M. Poostchi, G. Seetharaman, and K. Palaniappan

Persistent target tracking using likelihood fusion in wide-area and full motion video sequences

15th Int. Conf. Information Fusion, pgs. 2420--2427, 2012

wami, tracking, fmv, motion, features, fusion, dod

Abstract, Bibtex, PlainText, PDF, URL, Google Scholar

@inproceedings{Rengarajan:LOFTSystem_FUSION2012,
  author = "R. Pelapur and S. Candemir and F. Bunyak and M. Poostchi and G. Seetharaman and K. Palaniappan",
  title = "Persistent target tracking using likelihood fusion in wide-area and full motion video sequences",
  year = 2012,
  booktitle = "15th Int. Conf. Information Fusion",
  pages = "2420--2427",
  keywords = "wami, tracking, fmv, motion, features, fusion, dod",
  url = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6290597"
}

R. Pelapur, S. Candemir, F. Bunyak, M. Poostchi, G. Seetharaman, and K. Palaniappan. Persistent target tracking using likelihood fusion in wide-area and full motion video sequences. 15th Int. Conf. Information Fusion, pages 2420--2427, 2012.

#144: V. B. S. Prasath, F. Bunyak, P. Dale, S. R. Frazier, and K. Palaniappan

Segmentation of breast cancer tissue microarrays for computer-aided diagnosis in pathology

IEEE Healthcare Innovation Conference, 2012

histopathology, active contours, classification, features, texture, machine learning, data mining, biomedical

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{Surya:HIC-2012-histopath,
  author = "V. B. S. Prasath and F. Bunyak and P. Dale and S. R. Frazier and K. Palaniappan",
  title = "Segmentation of breast cancer tissue microarrays for computer-aided diagnosis in pathology",
  year = 2012,
  booktitle = "IEEE Healthcare Innovation Conference",
  keywords = "histopathology, active contours, classification, features, texture, machine learning, data mining, biomedical"
}

V. B. S. Prasath, F. Bunyak, P. Dale, S. R. Frazier, and K. Palaniappan. Segmentation of breast cancer tissue microarrays for computer-aided diagnosis in pathology. IEEE Healthcare Innovation Conference, 2012.

#156: S. Candemir, S. Jaeger, K. Palaniappan, S. Antani, and G. Thoma

Graph cut based automatic lung boundary detection in chest radiographs

IEEE Healthcare Innovation Conference, 2012

registration, segmentation, graph methods, anatomical, biomedical

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{Sema:HIC-2012-lung-seg,
  author = "S. Candemir and S. Jaeger and K. Palaniappan and S. Antani and G. Thoma",
  title = "Graph cut based automatic lung boundary detection in chest radiographs",
  year = 2012,
  booktitle = "IEEE Healthcare Innovation Conference",
  keywords = "registration, segmentation, graph methods, anatomical, biomedical"
}

S. Candemir, S. Jaeger, K. Palaniappan, S. Antani, and G. Thoma. Graph cut based automatic lung boundary detection in chest radiographs. IEEE Healthcare Innovation Conference, 2012.

#151: V. B. S. Prasath, K. Palaniappan, and G. Seetharaman

Multichannel texture image segmentation using local feature fitting based variational active contours

8th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), 2012

active contours, color, texture, segmentation, features, fusion

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Surya:ICVGIP-2012-local-texture,
  author = "V. B. S. Prasath and K. Palaniappan and G. Seetharaman",
  title = "Multichannel texture image segmentation using local feature fitting based variational active contours",
  year = 2012,
  booktitle = "8th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP)",
  keywords = "active contours, color, texture, segmentation, features, fusion",
  doi = "10.1145/2425333.2425411"
}

V. B. S. Prasath, K. Palaniappan, and G. Seetharaman. Multichannel texture image segmentation using local feature fitting based variational active contours. 8th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), 2012.

#158: S. Candemir, K. Palaniappan, F. Bunyak, G. Seetharaman, and R. Rao

Feature prominence-based weighting scheme for video tracking

8th Indian Conference on Computer Vision, Graphics and Image Processing, 2012

wami, tracking, fmv, motion, features, fusion, dod

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Sema:ICVGIP-2012-feature-prom,
  author = "S. Candemir and K. Palaniappan and F. Bunyak and G. Seetharaman and R. Rao",
  title = "Feature prominence-based weighting scheme for video tracking",
  year = 2012,
  booktitle = "8th Indian Conference on Computer Vision, Graphics and Image Processing",
  keywords = "wami, tracking, fmv, motion, features, fusion, dod",
  doi = "10.1145/2425333.2425358"
}

S. Candemir, K. Palaniappan, F. Bunyak, G. Seetharaman, and R. Rao. Feature prominence-based weighting scheme for video tracking. 8th Indian Conference on Computer Vision, Graphics and Image Processing, 2012.

#146: M. Poostchi, K. Palaniappan, F. Bunyak, M. Becchi, and G. Seetharaman

Realtime motion detection based on the spatio-temporal median filter using GPU integral histograms

8th Indian Conference on Computer Vision, Graphics and Image Processing, 2012

parallelization, gpu, tracking, fmv, motion, features, dod

Abstract, Bibtex, PlainText, PDF, DOI, Google Scholar

@inproceedings{Mahdieh:ICVGIP-2012-median,
  author = "M. Poostchi and K. Palaniappan and F. Bunyak and M. Becchi and G. Seetharaman",
  title = "Realtime motion detection based on the spatio-temporal median filter using GPU integral histograms",
  year = 2012,
  booktitle = "8th Indian Conference on Computer Vision, Graphics and Image Processing",
  keywords = "parallelization, gpu, tracking, fmv, motion, features, dod",
  doi = "10.1145/2425333.2425352"
}

M. Poostchi, K. Palaniappan, F. Bunyak, M. Becchi, and G. Seetharaman. Realtime motion detection based on the spatio-temporal median filter using GPU integral histograms. 8th Indian Conference on Computer Vision, Graphics and Image Processing, 2012.

#150: L. Wang, C. Shen, G. Seetharaman, K. Palaniappan, and S. Bhattacharyya

Multidimensional dataflow graph modeling and mapping for efficient GPU implementation

IEEE Workshop Signal Processing Systems (SiPS), pgs. 300--305, 2012

parallelization, gpu, tracking, fmv, motion, features, dod

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@inproceedings{Shuvra:SIPS-2012-IH,
  author = "L. Wang and C. Shen and G. Seetharaman and K. Palaniappan and S. Bhattacharyya",
  title = "Multidimensional dataflow graph modeling and mapping for efficient GPU implementation",
  year = 2012,
  booktitle = "IEEE Workshop Signal Processing Systems (SiPS)",
  pages = "300--305",
  keywords = "parallelization, gpu, tracking, fmv, motion, features, dod",
  doi = "10.1109/SiPS.2012.10",
  url = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6363272"
}

L. Wang, C. Shen, G. Seetharaman, K. Palaniappan, and S. Bhattacharyya. Multidimensional dataflow graph modeling and mapping for efficient GPU implementation. IEEE Workshop Signal Processing Systems (SiPS), pages 300--305, 2012.

#120: K. Palaniappan, R. Rao, and G. Seetharaman

Wide-area persistent airborne video: Architecture and challenges

Distributed Video Sensor Networks: Research Challenges and Future Directions, Springer, pgs. 349--371, 2011

wami, tracking, fmv, features, motion, fusion, dod

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@incollection{Pal:DVSNbook-2011,
  author = "K. Palaniappan and R. Rao and G. Seetharaman",
  title = "Wide-area persistent airborne video: Architecture and challenges",
  year = 2011,
  booktitle = "Distributed Video Sensor Networks: Research Challenges and Future Directions",
  publisher = "Springer",
  pages = "349--371",
  keywords = "wami, tracking, fmv, features, motion, fusion, dod",
  doi = "10.1007/978-0-85729-127-1_24",
  url = "http://link.springer.com/chapter/10.1007/978-0-85729-127-1_24"
}

K. Palaniappan, R. Rao, and G. Seetharaman. Wide-area persistent airborne video: Architecture and challenges. Distributed Video Sensor Networks: Research Challenges and Future Directions, Springer, pages 349--371, 2011.

#134: F. Bunyak, A. Hafiane, and K. Palaniappan

Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets

Software Tools and Algorithms for Biological Systems, Springer, pgs. 413--424, 2011

histopathology, active contours, classification, features, texture, machine learning, data mining, biomedical

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@incollection{Bunyak:histo-book-2011,
  author = "F. Bunyak and A. Hafiane and K. Palaniappan",
  title = "Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets",
  year = 2011,
  booktitle = "Software Tools and Algorithms for Biological Systems",
  publisher = "Springer",
  pages = "413--424",
  keywords = "histopathology, active contours, classification, features, texture, machine learning, data mining, biomedical",
  doi = "10.1007/978-1-4419-7046-6_41",
  url = "http://link.springer.com/chapter/10.1007/978-1-4419-7046-6_41"
}

F. Bunyak, A. Hafiane, and K. Palaniappan. Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets. Software Tools and Algorithms for Biological Systems, Springer, pages 413--424, 2011.

#127: P. Bellens, K. Palaniappan, R. M. Badia, G. Seetharaman, and J. Labarta

Parallel implementation of the integral histogram

Lecture Notes in Computer Science (ACIVS), Volume 6915, pgs. 586--598, 2011

parallelization, gpu, tracking, fmv, motion, features, dod

Abstract, Bibtex, PlainText, PDF, URL, DOI, Google Scholar

@article{Bellens:ACIVS-2011,
  author = "P. Bellens and K. Palaniappan and R. M. Badia and G. Seetharaman and J. Labarta",
  title = "Parallel implementation of the integral histogram",
  year = 2011,
  journal = "Lecture Notes in Computer Science (ACIVS)",
  volume = 6915,
  pages = "586--598",
  keywords = "parallelization, gpu, tracking, fmv, motion, features, dod",
  doi = "10.1007/978-3-642-23687-7_53",
  url = "http://link.springer.com/chapter/10.1007/978-3-642-23687-7_53"
}

P. Bellens, K. Palaniappan, R. M. Badia, G. Seetharaman, and J. Labarta. Parallel implementation of the integral histogram. Lecture Notes in Computer Science (ACIVS), volume 6915, pages 586--598, 2011.

275 Publications by "K. K. Palaniappan"

deep learning, vocal folds, laryngeal endoscopy, laryngeal closure detection, laryngeal adductor reflex

3d voxel rendering, denoising, convolutional neural networks, synthetic aerial imagery, point cloud evaluation

malaria diagnosis, plasmodium falciparum, plas-modium vivax, microscopy, thin blood smear, machine learning, image analysis

moving object, change detection, background subtraction, flux tensor, convolutional neural networks, u-net

vessel segmentation, confocal microscopy images, deep learning, semantic segmentation

jaw movement, food intake, behavior, neuronal migration, axon guidance, zebrafish, neural circuits, facial branchiomotor neuron, fusion, biomedical

training, image segmentation, shape, microscopy, stem cells, cell detection, cell tracking, mitosis

edge/fog computation offloading, drone video analytics, mobile edge computing, learning-based scheme, data processing in fog computing

structure-from-motion (sfm), video stabilization, image registration, orthorectification, mosaicking

deep learning, fusion, wami, object detection, multi-object tracking, dod

local feature detector, sift, surf, orb, akaze binary feature, mser, lf-net, superpoint, image matching, photogrammetry, camera pose, bundle adjustment, simulation environments, virtual reality, augmented reality

deepfake detection, deep learning, multi-modal, computer vision

3d stereo, aerial video, convolutional neural network, cnn, dct, feature descriptor, matching, point correspondences, spatial frequency

normalized cross correlation, multi-threading, gpu

drone-based multiple object tracking, drone performance evaluation, multi-object tracking

ground truth generator, object annotation, deep learning, object detection, tracking

unsupervised, convolutional neural network, homography estimation, uav freely flown, maize, mosaicking

video stabilization, image registration, georegistration, camera parameter optimization, homography, parallax, 3d

global mosaicing, registration, normalize cross correlation, fuse descriptor, mesentery

aerial imagery, crop field imagery, maize, mosaicking, small unmanned aerial system (suas), video summarization

drone video analytics, occlusion detection, multimedia networking protocols, computation off-loading, network management

normalize cross correlation, gradient blending, mosaicing, registration, biomedical, mesentery

ground-truth generation, visual tracking, crowd sourcing, annotation, deep learning

image restoration, structure tensor, variable exponent, anisotropic diffusion

jpeg compression, feature detection, 3d reconstruction, evaluation

dura mater, lymphatics, vascular structures, microvessels, pachymeningeal

semantic vessel segmentation, deep learning, histogram equalization, random forests, u-net

facial component segmentation, facial expression recognition, convolutional neural network, deep learning

drone-based multiple object tracking, drone performance evaluation, multi-object tracking

deep learning, object detection, data compression, video surveillance, image motion analysis, saliency

packet-loss, streaming video, interpolation

deep learning, object detection, data compression, video surveillance, image motion analysis

features, mosaicing, performance evaluation, aerial video

object-tracking, video analytics, animal behavior, automated data collection, tracking software

feature extraction, random forests, local features, deep learning, image classification

qoe-oriented cloud service orchestration, multiconstrained path-aware possibilistic c-mean clustering, 3q interplay model, least cost-disruptive decision tree

mobile edge computing, policy-based cloud management, energy-aware edge routing, low-latency visual data processing

cultural heritage, wide-area motion imagery, annotation tool, epipolar geometry

3d patch-based mvs, wami, 3d reconstruction, photometric consistency

fog computing, machine learning, predictive analytics, software-defined networking, experimental testbed

aerial imagery, mosaicking, registration, summarization, feature based registration.

visual object tracking, staple, correlation filter, color name, fusion

feature matching, evaluation, structure-from-motion, 3d reconstruction

drone-based multiple object tracking, drone performance evaluation, multi-object tracking

multi-object tracking, tracking, occlusion handling, association

tracking, wide area motion imagery, multi-object tracking, tracking-by-detection, data association

multi-object tracking, tracking-by-detection, global data association, local data association, appearance modules

zebrafish, branchiomotor circuit, behavior, video microscopy, optical flow, background subtraction, motion map

rootflow, stripflow, biology

rootflow, stripflow, biology

deep learning, vessel segmentation, fundoscopy images, patches, convolution neural network

convolutional neural networks, deep learning, facial component segmentation, facial expression recognition

plant seed morphometry, image analysis, mobile application

deep learning, segmentation, vasculature, biomedical

cameras, image registration, remote sensing, 3d reconstruction, mavmap, parallax-tolerant aerial image georegistration, pix4d, sfm method, structure from motion, visualsfm, camera metadata measurement, camera pose refinement, camera-induce

3d reconstruction, depth sensor, sensor fusion

3d reconstruction, depth sensor, sensor fusion

auto-calibration, planar scene reconstruction, metadata recovery, aerial imagery

registration, 3d reconstruction

biometrics, boosting, classification, convergence, data fusion, multiarmed bandits, multiview learning

iot-based applications, cloud computing, energy awareness, visual data processing, mobile edge cloud