Tracking moving objects in simulations and video: Detection and tracking of moving objects are important tasks in problems such as activity detection and identification in video sequences. We explored a range of techniques, focusing on how to make them more robust and computationally efficient so we could detect and track a moderate number of vehicles in video from traffic sequences, as well as a large number of non-rigid, coherent structures in spatio-temporal data from simulations.
Select publications (available from Google Scholar):
- A. Gezahegne and C. Kamath, “Tracking non-rigid structures in computer simulations,” IEEE International Conference on Image Processing, San Diego, October 2008, pp. 1548-1551.
- Samson S.-C. Cheung and C. Kamath, “Robust background subtraction with foreground validation for urban traffic video,” Eurasip Journal on applied signal processing. Volume 14, pp 2330-2340, 2005.
- C. Kamath, A. Gezahegne, S. Newsam, G.M. Roberts, “Salient Points for Tracking Moving Objects in Video,” Proceedings, Image and Video Communications and Processing, pp 442-453, SPIE Volume 5685, Electronic Imaging, San Jose, January 2005.
- Cheung, S.-C., and C. Kamath, “Robust techniques for background subtraction in urban traffic video,” Video Communications and Image Processing, Volume 5308, pp 881-892, SPIE Electronic Imaging, San Jose, January 2004
- Gyaourova, A., C. Kamath, and S.-C. Cheung, “Block matching for object tracking,” LLNL Technical report, October 2003. UCRL-TR-200271.
PDEs for image processing: Partial differential equations have been used for image processing tasks such as denoising and segmentation. In some of our early work, we evaluated the performance of these methods on real images so we could understand better their pros and cons and compare them with more traditional methods of image processing. We were particularly interested in the computational cost of the PDE-based methods and the choice of various parameters and options in their implementation.
Select publications (available from Google Scholar):
- Weeratunga S. and C. Kamath, “An investigation of implicit active contours for scientific image segmentation,” Video Communications and Image Processing, SPIE Volume 5308, pp. 210-221, SPIE Electronic Imaging, San Jose, January 2004.
- Weeratunga S.K., and C. Kamath, “A comparison of PDE-based non-linear anisotropic diffusion techniques for image denoising,” Proceedings, Image Processing: Algorithms and Systems II, SPIE Electronic Imaging, San Jose, January 2003.
- Weeratunga S.K. and C. Kamath, “PDE-based non-linear diffusion techniques for denoising scientific/industrial images: An empirical study,” Proceedings, Image Processing: Algorithms and Systems, SPIE Electronic Imaging, pp. 279-290, San Jose, January 2002.
Sapphire scientific data mining software (R&D100 award, 2006): When I started the Sapphire scientific data mining project in late 1990s, I put together an object-oriented, modular design for the software. A common interface for each class of algorithms allowed us to easily try different algorithms and create new ones, a common data store supported many different data formats used in different domains, and the implementation of the compute intensive parts in C++, that were glued together using Python, enabled us to quickly create efficient solutions to specific problems. Nearly twenty-five years later, with lots more practical experience in data mining, I find the approach I took provided both the flexibility and efficiency required to meet the diverse needs of data analysis in scientific simulations, observations and experiments.
Select publications (available from Google Scholar):
- C. Kamath, Scientific Data Mining: A Practical Perspective, SIAM, Philadelphia, May 2009.
- Kamath, C., “Sapphire System Architecture,” IPAM short program on Mathematical Challenges in Scientific Data Mining, UCLA, January 14-18, 2002. UCRL-PRES-146654.
- Kamath, Chandrika, and Erick Cantú-Paz, “On the design of a parallel object-oriented data mining toolkit,” Workshop on Distributed and Parallel Knowledge Discovery, Knowledge Discovery and Data Mining Conference, Boston, August 20-23, 2000.