Computational Imaging & VisAnalysis

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

Deep Learning Based Landmark Matching For Aerial Geolocalization

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

Bibtex, PlainText, PDF, DOI, Google Scholar

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

#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.

#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.

#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.

#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.

#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.

#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.

#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.

10 Publications by "R. Rao"

wami, tracking

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

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

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

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

parallelization, wami, tracking, fmv, motion, dod

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

parallelization, wami, tracking, fmv, motion, dod