Computational Imaging & VisAnalysis

#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.

#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.

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

The Visual Object Tracking VOT2015 challenge results

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

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

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

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

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

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

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

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

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

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

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

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

#164: R. Pelapur, F. Bunyak, G. Seetharaman, and K. Palaniappan

Vehicle detection and orientation estimation using the Radon transform

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

wami, tracking, fmv, motion, dod

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

@inproceedings{Rengarajan:Orientation-estim_SPIE-2013,
  author = "R. Pelapur and F. Bunyak and G. Seetharaman and K. Palaniappan",
  title = "Vehicle detection and orientation estimation using the Radon transform",
  year = 2013,
  booktitle = "Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation)",
  volume = 8747,
  pages = "87470I",
  keywords = "wami, tracking, fmv, motion, dod",
  doi = "10.1117/12.2016407"
}

R. Pelapur, F. Bunyak, G. Seetharaman, and K. Palaniappan. Vehicle detection and orientation estimation using the Radon transform. Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation), volume 8747, pages 87470I, 2013.

#173: M. Poostchi, F. Bunyak, and K. Palaniappan

Feature selection for appearance-based vehicle tracking in geospatial video

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

wami, tracking, fmv, features, motion, dod

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

@inproceedings{Mahdieh:FeatureSelection_SPIE-2013,
  author = "M. Poostchi and F. Bunyak and K. Palaniappan",
  title = "Feature selection for appearance-based vehicle tracking in geospatial video",
  year = 2013,
  booktitle = "Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation)",
  volume = 8747,
  pages = "87470G",
  keywords = "wami, tracking, fmv, features, motion, dod",
  doi = "10.1117/12.2015672"
}

M. Poostchi, F. Bunyak, and K. Palaniappan. Feature selection for appearance-based vehicle tracking in geospatial video. Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation), volume 8747, pages 87470G, 2013.

#168: J. Fraser, A. Haridas, G. Seetharaman, R. Rao, and K. Palaniappan

KOLAM: A cross-platform architecture for scalable visualization and tracking in wide-area motion imagery

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

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

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

KOLAM is an open, cross-platform, interoperable, scalable and extensible framework supporting a novel multi- scale spatiotemporal dual-cache data structure for big data visualization and visual analytics. This paper focuses on the use of KOLAM for target tracking in high-resolution, high throughput wide format video also known as wide-area motion imagery (WAMI). It was originally developed for the interactive visualization of extremely large geospatial imagery of high spatial and spectral resolution. KOLAM is platform, operating system and (graphics) hardware independent, and supports embedded datasets scalable from hundreds of gigabytes to feasibly petabytes in size on clusters, workstations, desktops and mobile computers. In addition to rapid roam, zoom and hyper- jump spatial operations, a large number of simultaneously viewable embedded pyramid layers (also referred to as multiscale or sparse imagery), interactive colormap and histogram enhancement, spherical projection and terrain maps are supported. The KOLAM software architecture was extended to support airborne wide-area motion imagery by organizing spatiotemporal tiles in very large format video frames using a temporal cache of tiled pyramid cached data structures. The current version supports WAMI animation, fast intelligent inspection, trajectory visualization and target tracking (digital tagging); the latter by interfacing with external automatic tracking software. One of the critical needs for working with WAMI is a supervised tracking and visualization tool that allows analysts to digitally tag multiple targets, quickly review and correct tracking results and apply geospatial visual analytic tools on the generated trajectories. One-click manual tracking combined with multiple automated tracking algorithms are available to assist the analyst and increase human effectiveness. Keywords: wide-area motion imagery, WAMI, digital tagging, assisted tracking, trajectory visualization, tra- jectory data mining, spatiotemporal cache, multiresolution tiled pyramid imagery

@inproceedings{Fraser:KOLAM_SPIE-2013,
  author = "J. Fraser and A. Haridas and G. Seetharaman and R. Rao and K. Palaniappan",
  title = "KOLAM: A cross-platform architecture for scalable visualization and tracking in wide-area motion imagery",
  year = 2013,
  booktitle = "Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation)",
  volume = 8747,
  pages = "87470N",
  keywords = "visualization, big data, wami, tracking, fmv, features, motion, dod, biomedical",
  doi = "10.1117/12.2018162"
}

J. Fraser, A. Haridas, G. Seetharaman, R. Rao, and K. Palaniappan. KOLAM: A cross-platform architecture for scalable visualization and tracking in wide-area motion imagery. Proc. SPIE Conf. Geospatial InfoFusion III (Defense, Security and Sensing: Sensor Data and Information Exploitation), volume 8747, pages 87470N, 2013.

#140: E. Blasch, G. Seetharaman, K. Palaniappan, H. Ling, and G. Chen

Wide-Area Motion Imagery (WAMI) exploitation tools for enhanced situational awareness

Proc. IEEE Applied Imagery Pattern Recognition Workshop, 2012

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

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

@inproceedings{Blasch:wami-tools-2012,
  author = "E. Blasch and G. Seetharaman and K. Palaniappan and H. Ling and G. Chen",
  title = "Wide-Area Motion Imagery (WAMI) exploitation tools for enhanced situational awareness",
  year = 2012,
  booktitle = "Proc. IEEE Applied Imagery Pattern Recognition Workshop",
  keywords = "wami, visual events, fusion, data mining, fmv, dod",
  url = "http://www.dabi.temple.edu/~hbling/publication/AIPR12_WAMI_for_E2ATv4_BW.pdf"
}

E. Blasch, G. Seetharaman, K. Palaniappan, H. Ling, and G. Chen. Wide-Area Motion Imagery (WAMI) exploitation tools for enhanced situational awareness. Proc. IEEE Applied Imagery Pattern Recognition Workshop, 2012.

#148: A. Camargo, Q. He, and K. Palaniappan

Performance evaluation of optimization methods for super-resolution mosaicking on UAS surveillance videos

Proc. SPIE Infrared Imaging Systems: Design, Analysis, Modeling and Testing XXIII, Volume 8355, 2012

image enhancement, wami, visual events, fusion, fmv, dod

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

@inproceedings{Camargo:super-resolution_SPIE-2012,
  author = "A. Camargo and Q. He and K. Palaniappan",
  title = "Performance evaluation of optimization methods for super-resolution mosaicking on UAS surveillance videos",
  year = 2012,
  booktitle = "Proc. SPIE Infrared Imaging Systems: Design, Analysis, Modeling and Testing XXIII",
  volume = 8355,
  keywords = "image enhancement, wami, visual events, fusion, fmv, dod",
  doi = "10.1117/12.919641",
  url = "http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1385560"
}

A. Camargo, Q. He, and K. Palaniappan. Performance evaluation of optimization methods for super-resolution mosaicking on UAS surveillance videos. Proc. SPIE Infrared Imaging Systems: Design, Analysis, Modeling and Testing XXIII, volume 8355, 2012.

#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.

#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.

#123: A. Haridas, R. Pelapur, J. Fraser, F. Bunyak, and K. Palaniappan

Visualization of automated and manual trajectories in wide-area motion imagery

15th Int. Conf. Information Visualization, pgs. 288--293, 2011

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

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

@inproceedings{Haridas:InfoVis-2011,
  author = "A. Haridas and R. Pelapur and J. Fraser and F. Bunyak and K. Palaniappan",
  title = "Visualization of automated and manual trajectories in wide-area motion imagery",
  year = 2011,
  booktitle = "15th Int. Conf. Information Visualization",
  pages = "288--293",
  keywords = "visualization, big data, wami, tracking, fmv, features, motion, dod",
  doi = "10.1109/IV.2011.67",
  url = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6004056"
}

A. Haridas, R. Pelapur, J. Fraser, F. Bunyak, and K. Palaniappan. Visualization of automated and manual trajectories in wide-area motion imagery. 15th Int. Conf. Information Visualization, pages 288--293, 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.

#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.

#93: S. Mehta, A. Misra, A. Singhal, P. Kumar, A. Mittal, and K. Palaniappan

Parallel implementation of video surveillance algorithms on GPU architectures using CUDA

17th IEEE Int. Conf. Advanced Computing and Communications (ADCOM), 2009

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

Abstract, Bibtex, PlainText, PDF, Google Scholar

@inproceedings{PKumar:ADCOM-gpu-2009,
  author = "S. Mehta and A. Misra and A. Singhal and P. Kumar and A. Mittal and K. Palaniappan",
  title = "Parallel implementation of video surveillance algorithms on GPU architectures using CUDA",
  year = 2009,
  booktitle = "17th IEEE Int. Conf. Advanced Computing and Communications (ADCOM)",
  keywords = "parallelization, gpu, tracking, fmv, motion, features, dod"
}

S. Mehta, A. Misra, A. Singhal, P. Kumar, A. Mittal, and K. Palaniappan. Parallel implementation of video surveillance algorithms on GPU architectures using CUDA. 17th IEEE Int. Conf. Advanced Computing and Communications (ADCOM), 2009.

#86: M. H. Kolekar and K. Palaniappan

A hierarchical framework for semantic scene classification in soccer sports video

IEEE Region Ten (TENCON) Int. Conference, pgs. Online, 2008

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

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

@inproceedings{Kolekar:TENCON-2008,
  author = "M. H. Kolekar and K. Palaniappan",
  title = "A hierarchical framework for semantic scene classification in soccer sports video",
  year = 2008,
  booktitle = "IEEE Region Ten (TENCON) Int. Conference",
  pages = "Online",
  keywords = "fmv, features, fusion, visual events, data mining, cbir",
  doi = "10.1109/TENCON.2008.4766547",
  url = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4766547"
}

M. H. Kolekar and K. Palaniappan. A hierarchical framework for semantic scene classification in soccer sports video. IEEE Region Ten (TENCON) Int. Conference, pages Online, 2008.

#72: M. H. Kolekar, K. Palaniappan, and S. Sengupta

Semantic event detection and classification in cricket video sequence

IEEE Indian Conference on Computer Vision, Graphics and Image Processing, pgs. 382--389, 2008

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

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

@inproceedings{Kolekar:CVGIP-2008,
  author = "M. H. Kolekar and K. Palaniappan and S. Sengupta",
  title = "Semantic event detection and classification in cricket video sequence",
  year = 2008,
  booktitle = "IEEE Indian Conference on Computer Vision, Graphics and Image Processing",
  pages = "382--389",
  keywords = "fmv, features, fusion, visual events, data mining, cbir",
  doi = "10.1109/ICVGIP.2008.102",
  url = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4756096"
}

M. H. Kolekar, K. Palaniappan, and S. Sengupta. Semantic event detection and classification in cricket video sequence. IEEE Indian Conference on Computer Vision, Graphics and Image Processing, pages 382--389, 2008.

#85: M. H. Kolekar, K. Palaniappan, and S. Sengupta

A novel framework for semantic annotation of soccer sports video sequences

Proc. 5th IEEE European Conference on Visual Media Production, pgs. 1-9, 2008

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

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

@inproceedings{Kolekar:CVMP-2008,
  author = "M. H. Kolekar and K. Palaniappan and S. Sengupta",
  title = "A novel framework for semantic annotation of soccer sports video sequences",
  year = 2008,
  booktitle = "Proc. 5th IEEE European Conference on Visual Media Production",
  pages = "1-9",
  keywords = "fmv, features, fusion, visual events, data mining, cbir",
  doi = "10.1049/cp:20081081",
  url = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4778745"
}

M. H. Kolekar, K. Palaniappan, and S. Sengupta. A novel framework for semantic annotation of soccer sports video sequences. Proc. 5th IEEE European Conference on Visual Media Production, pages 1-9, 2008.

#76: S. Grauer-Gray, C. Kambhamettu, and K. Palaniappan

GPU implementation of belief propagation using CUDA for cloud tracking and reconstruction

5th IAPR Workshop on Pattern Recognition in Remote Sensing (ICPR), pgs. 1--4, 2008

parallelization, gpu, tracking, stereo, fmv, motion, features, cloud

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

@inproceedings{Chandra:ICPRW-2008,
  author = "S. Grauer-Gray and C. Kambhamettu and K. Palaniappan",
  title = "GPU implementation of belief propagation using CUDA for cloud tracking and reconstruction",
  year = 2008,
  booktitle = "5th IAPR Workshop on Pattern Recognition in Remote Sensing (ICPR)",
  pages = "1--4",
  keywords = "parallelization, gpu, tracking, stereo, fmv, motion, features, cloud",
  doi = "10.1109/PRRS.2008.4783169",
  url = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4783169"
}

S. Grauer-Gray, C. Kambhamettu, and K. Palaniappan. GPU implementation of belief propagation using CUDA for cloud tracking and reconstruction. 5th IAPR Workshop on Pattern Recognition in Remote Sensing (ICPR), pages 1--4, 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.

#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.

36 publications with the keyword "fmv"

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

fmv, motion, features, image anlaysis, wami

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

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

wami, tracking, fmv, motion, dod

wami, tracking, fmv, features, motion, dod

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

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

image enhancement, wami, visual events, fusion, 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

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

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

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

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

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

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

parallelization, wami, tracking, fmv, motion, dod

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

parallelization, wami, tracking, fmv, motion, dod

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

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

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

parallelization, tracking, fmv, motion, features, dod

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

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

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

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

parallelization, gpu, tracking, stereo, fmv, motion, features, cloud

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

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