Computational Imaging & VisAnalysis

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

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

Computers in Biology and Medicine, pgs. 105339, 2022

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

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

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

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

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

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

5OTH ANNUAL APPLIED IMAGERY PATTERN RECOGNITION WORKSHOP, 2021

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

Abstract, Bibtex, PlainText, PDF, Google Scholar

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

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

#346: Y. Y. Wang, K. Gao, A. Hamad, B. McCarthy, A. M. Kloepper, T. E. Lever, and F. Bunyak

Multi-Modal and Multi-Scale Oral Diadochokinesis Analysis using Deep Learning

5OTH ANNUAL APPLIED IMAGERY PATTERN RECOGNITION WORKSHOP, 2021

oral diadochokinesis, syllable detection, mouth/jaw motion, deep learning

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{2021a,
  author = "Y. Y. Wang and K. Gao and A. Hamad and B. McCarthy and A. M. Kloepper and T. E. Lever and F. Bunyak",
  title = "Multi-Modal and Multi-Scale Oral Diadochokinesis Analysis using Deep Learning",
  year = 2021,
  booktitle = "5OTH ANNUAL APPLIED IMAGERY PATTERN RECOGNITION WORKSHOP",
  keywords = "oral diadochokinesis, syllable detection, mouth/jaw motion, deep learning"
}

Y. Y. Wang, K. Gao, A. Hamad, B. McCarthy, A. M. Kloepper, T. E. Lever, and F. Bunyak. Multi-Modal and Multi-Scale Oral Diadochokinesis Analysis using Deep Learning. 5OTH ANNUAL APPLIED IMAGERY PATTERN RECOGNITION WORKSHOP, 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.

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

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

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

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

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

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

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

#317: A. S. Hamad, Y. Y. Wang, T. E. Lever, and F. Bunyak

Ensemble of deep cascades for detection of laryngeal adductor reflex events in Endoscopy Videos

IEEE International Conference on Image Processing (ICIP), pgs. 300-304, 2020

deep learning, laryngeal adductor reflex, automated detection, endoscopy video analysis

Abstract, Bibtex, PlainText, DOI, Google Scholar

@inproceedings{Hamad:ICIP2020,
  author = "A. S. Hamad and Y. Y. Wang and T. E. Lever and F. Bunyak",
  title = "Ensemble of deep cascades for detection of laryngeal adductor reflex events in Endoscopy Videos",
  year = 2020,
  booktitle = "IEEE International Conference on Image Processing (ICIP)",
  pages = "300-304",
  keywords = "deep learning, laryngeal adductor reflex, automated detection, endoscopy video analysis",
  doi = "10.1109/ICIP40778.2020.9190792"
}

A. S. Hamad, Y. Y. Wang, T. E. Lever, and F. Bunyak. Ensemble of deep cascades for detection of laryngeal adductor reflex events in Endoscopy Videos. IEEE International Conference on Image Processing (ICIP), pages 300-304, 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.

#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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

#297: Y. Y. Wang, K. Gao, A. M. Kloepper, Y. Zhao, M. Kuruvilla-Dugdale, T. E. Lever, and F. Bunyak

DeepDDK: A deep learning based oral-diadochokinesis analysis software

IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pgs. 1--4, 2019

diadochokinesis analysis, speech signal analysis, deep learning, event detection, event localization

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

@inproceedings{wang2019deepddk,
  author = "Y. Y. Wang and K. Gao and A. M. Kloepper and Y. Zhao and M. Kuruvilla-Dugdale and T. E. Lever and F. Bunyak",
  title = "DeepDDK: A deep learning based oral-diadochokinesis analysis software",
  year = 2019,
  booktitle = "IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)",
  pages = "1--4",
  keywords = "diadochokinesis analysis, speech signal analysis, deep learning, event detection, event localization",
  doi = "10.1109/BHI.2019.8834506",
  url = "https://ieeexplore.ieee.org/abstract/document/8834506"
}

Y. Y. Wang, K. Gao, A. M. Kloepper, Y. Zhao, M. Kuruvilla-Dugdale, T. E. Lever, and F. Bunyak. DeepDDK: A deep learning based oral-diadochokinesis analysis software. IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pages 1--4, 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.

#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.

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

Extracting Retinal Vascular Networks Using Deep Learning Architecture

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

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

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

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

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

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

Deep learning-based facial expression recognition for monitoring neurological disorders

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

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

Abstract, Bibtex, PlainText, URL, Google Scholar

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

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

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

Microvasculature segmentation of arterioles using deep CNN

IEEE International Conference on Image Processing (ICIP), 2017

deep learning, segmentation, vasculature, biomedical

Abstract, Bibtex, PlainText, URL, Google Scholar

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

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

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

CNN - Based Image Analysis for Malaria Diagnosis

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

deep learning, malaira, cells, classification, biomedical

Abstract, Bibtex, PlainText, Google Scholar

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

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

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

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

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

deep learning, histopathology, segmentation

Bibtex, PlainText, URL, Google Scholar

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

19 publications with the keyword "deep learning"

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

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

oral diadochokinesis, syllable detection, mouth/jaw motion, deep learning

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

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

deep learning, laryngeal adductor reflex, automated detection, endoscopy video analysis

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

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

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

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

diadochokinesis analysis, speech signal analysis, deep learning, event detection, event localization

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

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

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

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

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

deep learning, segmentation, vasculature, biomedical

deep learning, malaira, cells, classification, biomedical

deep learning, histopathology, segmentation