Computational Imaging & VisAnalysis

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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,
  author = "H. Aliakbarpour and K. Palaniappan and G. Seetharaman",
  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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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

@article{Gargees:Cloud-CSVT-2016,
  author = "R. Gargees and B. Morago and R. Pelapur and D. Chemodanov and P. Calyam and Z. Oraibi and Y. Duan and G. Seetharaman and K. Palaniappan",
  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

@inproceedings{Meena:Aerial-SPIE-2016,
  author = "S. Meena and K. Palaniappan and G. Seetharaman and V. B. S. Prasath",
  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.

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

#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.

#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.

#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

@article{Adel:AdaptiveMBP-PR-2015,
  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.

#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

@inproceedings{Adel:AdaptiveLBP-ICPR-2014,
  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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#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.

#126: J. Peng, C. Barbu, G. Seetharaman, F. Wei, X. Wu, and K. Palaniappan

ShareBoost: Boosting for multi-view learning with performance guarantees

Lecture Notes in Artificial Intelligence (ECML PKDD), Volume 6912, pgs. 597--612, 2011

machine learning, detection, classification, features, texture, data mining

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

@article{JPeng:ECML-2011,
  author = "J. Peng and C. Barbu and G. Seetharaman and F. Wei and X. Wu and K. Palaniappan",
  title = "ShareBoost: Boosting for multi-view learning with performance guarantees",
  year = 2011,
  journal = "Lecture Notes in Artificial Intelligence (ECML PKDD)",
  volume = 6912,
  pages = "597--612",
  keywords = "machine learning, detection, classification, features, texture, data mining",
  doi = "10.1007/978-3-642-23783-6_38",
  url = "http://link.springer.com/chapter/10.1007/978-3-642-23783-6_38"
}

J. Peng, C. Barbu, G. Seetharaman, F. Wei, X. Wu, and K. Palaniappan. ShareBoost: Boosting for multi-view learning with performance guarantees. Lecture Notes in Artificial Intelligence (ECML PKDD), volume 6912, pages 597--612, 2011.

#128: K. Palaniappan, I. Ersoy, G. Seetharaman, S. Davis, P. Kumar, R. M. Rao, and R. Linderman

Parallel flux tensor analysis for efficient moving object detection

14th Int. Conf. Information Fusion, 2011

parallelization, wami, tracking, fmv, motion, dod

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

@inproceedings{Pal:flux_Fusion-2011,
  author = "K. Palaniappan and I. Ersoy and G. Seetharaman and S. Davis and P. Kumar and R. M. Rao and R. Linderman",
  title = "Parallel flux tensor analysis for efficient moving object detection",
  year = 2011,
  booktitle = "14th Int. Conf. Information Fusion",
  keywords = "parallelization, wami, tracking, fmv, motion, dod",
  url = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5977499"
}

K. Palaniappan, I. Ersoy, G. Seetharaman, S. Davis, P. Kumar, R. M. Rao, and R. Linderman. Parallel flux tensor analysis for efficient moving object detection. 14th Int. Conf. Information Fusion, 2011.

#136: E. Blasch, P. Deignan, S. Dockstader, M. Pellechia, K. Palaniappan, and G. Seetharaman

Contemporary concerns in Geographical/Geospatial Information Systems (GIS) processing

Proc. IEEE National Aerospace and Electronics Conference (NAECON), pgs. 183--190, 2011

wami, visual events, fusion, data mining, gis, dod

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

@inproceedings{Blasch:GIS-NAECON-2011,
  author = "E. Blasch and P. Deignan and S. Dockstader and M. Pellechia and K. Palaniappan and G. Seetharaman",
  title = "Contemporary concerns in Geographical/Geospatial Information Systems (GIS) processing",
  year = 2011,
  booktitle = "Proc. IEEE National Aerospace and Electronics Conference (NAECON)",
  pages = "183--190",
  keywords = "wami, visual events, fusion, data mining, gis, dod",
  doi = "10.1109/NAECON.2011.6183099",
  url = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6183099"
}

E. Blasch, P. Deignan, S. Dockstader, M. Pellechia, K. Palaniappan, and G. Seetharaman. Contemporary concerns in Geographical/Geospatial Information Systems (GIS) processing. Proc. IEEE National Aerospace and Electronics Conference (NAECON), pages 183--190, 2011.

#114: K. Palaniappan, F. Bunyak, P. Kumar, I. Ersoy, S. Jaeger, K. Ganguli, A. Haridas, J. Fraser, R. Rao, and G. Seetharaman

Efficient feature extraction and likelihood fusion for vehicle tracking in low frame rate airborne video

13th Int. Conf. Information Fusion, 2010

wami, tracking, fmv, motion, features, fusion, dod

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

@inproceedings{Pal:Fusion-2010,
  author = "K. Palaniappan and F. Bunyak and P. Kumar and I. Ersoy and S. Jaeger and K. Ganguli and A. Haridas and J. Fraser and R. Rao and G. Seetharaman",
  title = "Efficient feature extraction and likelihood fusion for vehicle tracking in low frame rate airborne video",
  year = 2010,
  booktitle = "13th Int. Conf. Information Fusion",
  keywords = "wami, tracking, fmv, motion, features, fusion, dod",
  doi = "10.1109/ICIF.2010.5711891",
  url = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5711891"
}

K. Palaniappan, F. Bunyak, P. Kumar, I. Ersoy, S. Jaeger, K. Ganguli, A. Haridas, J. Fraser, R. Rao, and G. Seetharaman. Efficient feature extraction and likelihood fusion for vehicle tracking in low frame rate airborne video. 13th Int. Conf. Information Fusion, 2010.

#110: K. Palaniappan, I. Ersoy, G. Seetharaman, S. Davis, R. Rao, and R. Linderman

Multicore energy efficient flux tensor for video analysis

IEEE Workshop on Energy Efficient High-Performance Computing (EEHiPC), 2010

parallelization, wami, tracking, fmv, motion, dod

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

@inproceedings{Pal:EEHiPC-2010,
  author = "K. Palaniappan and I. Ersoy and G. Seetharaman and S. Davis and R. Rao and R. Linderman",
  title = "Multicore energy efficient flux tensor for video analysis",
  year = 2010,
  booktitle = "IEEE Workshop on Energy Efficient High-Performance Computing (EEHiPC)",
  keywords = "parallelization, wami, tracking, fmv, motion, dod",
  url = "http://nsfcac.rutgers.edu/GreenHPC/EEHiPC/eehipc_palaniappan.pdf"
}

K. Palaniappan, I. Ersoy, G. Seetharaman, S. Davis, R. Rao, and R. Linderman. Multicore energy efficient flux tensor for video analysis. IEEE Workshop on Energy Efficient High-Performance Computing (EEHiPC), 2010.

#88: M. H. Kolekar, K. Palaniappan, S. Sengupta, and G. Seetharaman

Semantic concept mining based on hierarchical event detection for soccer video indexing

J. Multimedia, Volume 4, pgs. 298--312, 2009

fmv, motion, features, fusion, visual events, data mining, cbir

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

@article{Kolekar:JMM-2009,
  author = "M. H. Kolekar and K. Palaniappan and S. Sengupta and G. Seetharaman",
  title = "Semantic concept mining based on hierarchical event detection for soccer video indexing",
  year = 2009,
  journal = "J. Multimedia",
  volume = 4,
  number = 5,
  pages = "298--312",
  month = "Oct",
  keywords = "fmv, motion, features, fusion, visual events, data mining, cbir",
  doi = "10.4304/jmm.4.5.298-312",
  url = "http://ojs.academypublisher.com/index.php/jmm/article/view/2255"
}

M. H. Kolekar, K. Palaniappan, S. Sengupta, and G. Seetharaman. Semantic concept mining based on hierarchical event detection for soccer video indexing. J. Multimedia, volume 4, issue 5, pages 298--312, October 2009.

#96: K. Palaniappan, I. Ersoy, A. Hafiane, and G. Seetharaman

Moving object detection in UAV-video using flux tensors

IEEE Int. Geoscience and Remote Sensing Symposium, 2009

wami, tracking, fmv, registration, motion, fusion, dod

Bibtex, PlainText, Google Scholar

@inproceedings{Hafiane:IGARSS-2009,
  author = "K. Palaniappan and I. Ersoy and A. Hafiane and G. Seetharaman",
  title = "Moving object detection in UAV-video using flux tensors",
  year = 2009,
  booktitle = "IEEE Int. Geoscience and Remote Sensing Symposium",
  keywords = "wami, tracking, fmv, registration, motion, fusion, dod"
}

#100: M. H. Kolekar, K. Palaniappan, S. Sengupta, and G. Seetharaman

Event detection and semantic identification using Bayesian belief networks

IEEE Int. Conf. Computer Vision Workshops, pgs. 554--561, 2009

fmv, motion, features, fusion, visual events, data mining, cbir

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

@inproceedings{Kolekar:ICCVW-2009,
  author = "M. H. Kolekar and K. Palaniappan and S. Sengupta and G. Seetharaman",
  title = "Event detection and semantic identification using Bayesian belief networks",
  year = 2009,
  booktitle = "IEEE Int. Conf. Computer Vision Workshops",
  pages = "554--561",
  keywords = "fmv, motion, features, fusion, visual events, data mining, cbir",
  doi = "10.1109/ICCVW.2009.5457652",
  url = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5457652"
}

M. H. Kolekar, K. Palaniappan, S. Sengupta, and G. Seetharaman. Event detection and semantic identification using Bayesian belief networks. IEEE Int. Conf. Computer Vision Workshops, pages 554--561, 2009.

#94: P. Kumar, K. Palaniappan, A. Mittal, and G. Seetharaman

Parallel blob extraction using the multi-core Cell processor

Lecture Notes in Computer Science (ACIVS), Volume 5807, pgs. 320--332, 2009

parallelization, tracking, fmv, motion, features, dod

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

@article{PKumar:ACIVS-2009,
  author = "P. Kumar and K. Palaniappan and A. Mittal and G. Seetharaman",
  title = "Parallel blob extraction using the multi-core Cell processor",
  year = 2009,
  journal = "Lecture Notes in Computer Science (ACIVS)",
  volume = 5807,
  pages = "320--332",
  keywords = "parallelization, tracking, fmv, motion, features, dod",
  doi = "10.1007/978-3-642-04697-1_30",
  url = "http://link.springer.com/chapter/10.1007%2F978-3-642-04697-1_30"
}

P. Kumar, K. Palaniappan, A. Mittal, and G. Seetharaman. Parallel blob extraction using the multi-core Cell processor. Lecture Notes in Computer Science (ACIVS), volume 5807, pages 320--332, 2009.

#73: A. Hafiane, G. Seetharaman, K. Palaniappan, and B. Zavidovique

Rotationally invariant hashing of median binary patterns for texture classification

Lecture Notes in Computer Science (ICIAR), Volume 5112, pgs. 619--629, 2008

texture, image enhancement, classification, segmentation, cbir

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

@article{Hafiane:ICIAR-2008,
  author = "A. Hafiane and G. Seetharaman and K. Palaniappan and B. Zavidovique",
  title = "Rotationally invariant hashing of median binary patterns for texture classification",
  year = 2008,
  journal = "Lecture Notes in Computer Science (ICIAR)",
  volume = 5112,
  pages = "619--629",
  keywords = "texture, image enhancement, classification, segmentation, cbir",
  doi = "10.1007/978-3-540-69812-8_61",
  url = "http://link.springer.com/chapter/10.1007%2F978-3-540-69812-8_61"
}

A. Hafiane, G. Seetharaman, K. Palaniappan, and B. Zavidovique. Rotationally invariant hashing of median binary patterns for texture classification. Lecture Notes in Computer Science (ICIAR), volume 5112, pages 619--629, 2008.

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

UAV-Video registration using block-based features

IEEE Int. Geoscience and Remote Sensing Symposium, Volume II, pgs. 1104-1107, 2008

wami, tracking, fmv, registration, motion, fusion, dod

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

@inproceedings{Hafiane:IGARSS-2008,
  author = "A. Hafiane and K. Palaniappan and G. Seetharaman",
  title = "UAV-Video registration using block-based features",
  year = 2008,
  booktitle = "IEEE Int. Geoscience and Remote Sensing Symposium",
  volume = II,
  pages = "1104-1107",
  keywords = "wami, tracking, fmv, registration, motion, fusion, dod",
  doi = "10.1109/IGARSS.2008.4779192",
  url = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4779192"
}

A. Hafiane, K. Palaniappan, and G. Seetharaman. UAV-Video registration using block-based features. IEEE Int. Geoscience and Remote Sensing Symposium, volume II, pages 1104-1107, 2008.

#69: F. Bunyak, K. Palaniappan, S. K. Nath, and G. Seetharaman

Flux tensor constrained geodesic active contours with sensor fusion for persistent object tracking

J. Multimedia, Volume 2, pgs. 20--33, 2007

motion, fusion, tracking, active contours, visual events, features

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

@article{Bunyak:JMM-2007,
  author = "F. Bunyak and K. Palaniappan and S. K. Nath and G. Seetharaman",
  title = "Flux tensor constrained geodesic active contours with sensor fusion for persistent object tracking",
  year = 2007,
  journal = "J. Multimedia",
  volume = 2,
  number = 4,
  pages = "20--33",
  month = "Aug",
  keywords = "motion, fusion, tracking, active contours, visual events, features",
  url = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2605095/"
}

F. Bunyak, K. Palaniappan, S. K. Nath, and G. Seetharaman. Flux tensor constrained geodesic active contours with sensor fusion for persistent object tracking. J. Multimedia, volume 2, issue 4, pages 20--33, August 2007.

#68: F. Bunyak, K. Palaniappan, S. K. Nath, and G. Seetharaman

Geodesic active contour based fusion of visible and infrared video for persistent object tracking

8th IEEE Workshop Applications of Computer Vision (WACV 2007), pgs. Online, 2007

motion, fusion, tracking, active contours, visual events, features

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

@inproceedings{Bunyak:WACV-2007,
  author = "F. Bunyak and K. Palaniappan and S. K. Nath and G. Seetharaman",
  title = "Geodesic active contour based fusion of visible and infrared video for persistent object tracking",
  year = 2007,
  booktitle = "8th IEEE Workshop Applications of Computer Vision (WACV 2007)",
  pages = "Online",
  keywords = "motion, fusion, tracking, active contours, visual events, features",
  doi = "10.1109/WACV.2007.26"
}

F. Bunyak, K. Palaniappan, S. K. Nath, and G. Seetharaman. Geodesic active contour based fusion of visible and infrared video for persistent object tracking. 8th IEEE Workshop Applications of Computer Vision (WACV 2007), pages Online, 2007.

#34: G. Seetharaman, G. Gasperas, and K. Palaniappan

A piecewise affine model for image registration in nonrigid motion analysis

IEEE Int. Conf. Image Processing, Volume 1, pgs. 561--564, 2000

wami, tracking, fmv, registration, motion, fusion, dod

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

@inproceedings{Guna:ICIP-2000,
  author = "G. Seetharaman and G. Gasperas and K. Palaniappan",
  title = "A piecewise affine model for image registration in nonrigid motion analysis",
  year = 2000,
  booktitle = "IEEE Int. Conf. Image Processing",
  volume = 1,
  pages = "561--564",
  keywords = "wami, tracking, fmv, registration, motion, fusion, dod",
  doi = "10.1109/ICIP.2000.901020",
  url = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=901020"
}

G. Seetharaman, G. Gasperas, and K. Palaniappan. A piecewise affine model for image registration in nonrigid motion analysis. IEEE Int. Conf. Image Processing, volume 1, pages 561--564, 2000.

98 Publications by "G. Seetharaman"

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

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

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

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

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

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

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

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

features, mosaicing, performance evaluation, aerial video

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

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

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

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

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

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

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

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

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

wami, tracking

haze removal, blending, mosaicing, wami

arst, edge detection, gpu

flux tensor, object detection, ftsg, patent

registration, 3d reconstruction, uncertainty modeling, sensor fusion

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

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

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

segmentation, interactive, gaussian elastic body, splines

segmentation, videos, active contours

cloud computing, resource management, tracking

interactive, segmentation, wami, target selection

segmentation, interactive, elastic body, splines

fmv, motion, features, image anlaysis, wami

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

video summarization, mosaicing, geospatial

3d reconstruction, wami, shape from motion

video summarization, mosaicing, geospatial, mobile, cognition

image restoration, regularization, anisotropic diffusion, total variation

texture, lbp, classification, cbir

texture, lbp, classification, cbir

tracking, multitarget, dod

segmentation, interactive, elastic body, splines

segmentation, active contours, video, globally convex

ftsg, flux tensor, split gaussian, change detection

wami, tracking, fmv, motion, dod

visualization, big data, wami, tracking, fmv, features, motion, dod, biomedical

wami, visual events, fusion, data mining, fmv, dod

parallelization, gpu, tracking, fmv, motion, features, dod

wami, tracking, fmv, features, motion, fusion, dod

wami, tracking, fmv, features, motion, fusion, dod

wami, tracking, fmv, features, motion, dod

wami, tracking, fmv, motion, features, dod

wami, tracking, fmv, features, motion, fusion, data mining, machine learning, dod

wami, tracking, fmv, motion, features, fusion, dod

active contours, color, texture, segmentation, features, fusion