Video based object tracking
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| Research Taxonomy |
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| Research Content |
Active contours -- or snakes -- are computer-generated curves that move within images to find object boundaries (note that the 3D version is often known as deformable models or active surfaces in the literature). They are often used in computer vision and image analysis to detect and locate objects, and to describe their shape.
Applications:
- Tracking identified person(s)/object(s) in smart service systems (CAMUS), films making, control systems (robotics).
- In biomedical images: tracking leukocytes, stem cells etc.
Our team has been doing research on motion tracking problems which usually have three following steps: |
1. Target segmentation using active contours (snakes) |
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The U-shaped object and the snake evolving to its boundary |
| 2. Motion field estimation |
| Tensor based technique |
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| 3. Motion tracking |
| Motion GVF techniques |
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The moving leukocyte and its motion tracking |
| References |
1. Denoising & enhancement
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2. Segmentation
[2.1] P. T. H. Truc, Y. H. Kim, Y.-K. Lee, S. Y. Lee, and T.-S. Kim, “Evaluation of Active contour-based techniques toward Bone segmentation for CT images”, in Springer IFMBE Proceedings, vol 14, pp. 2997-3000, 2006.
[2.2] P. T. H. Truc, T.-S. Kim, Y.-K. Lee, and S. Y. Lee “Automatic Bone Segmentation from CT Images using Chan-Vese Multiphase Active Contour”, J. Biomed. Eng. Res., vol 28, no. 6, pp. 713-720, 2007.
[2.3] L. Wang, M. Greenspan, and R. Ellis, “Validation of bone segmentation and improved 3-D registration using contour coherence in CT data”, IEEE Trans. Med. Imag., vol. 25, pp. 324-334, 2006.
[2.4] M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models”, Int. J. Comput. Vis., vol. 1, no. 4, pp. 321-331, 1988.
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[2.6] C. Xu and J. Prince, “Snakes, shapes, and gradient vector flow”, IEEE Trans. Image Proc., vol. 7, pp. 359-369, 1998.
[2.7] Caselles, F. Catte, T. Coll, and F. Dibos, “A geometric model for active contours”, Numerische Mathematik, vol. 66, pp. 1-31, 1993.
[2.8] V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours”, Int. J. Comp. Vis., vol. 22, pp. 61-79, 1997.
[2.9] N. Paragios, O.M-Gottardo, and V. Ramesh, “Gradient vector flow fast geometric active contours”, IEEE Trans. Patt. Anal. Mach. Intell., vol. 26, pp. 402-407, 2004.
[2.10] L. Vese and T. Chan, “A multiphase level set framework for image segmentation using Mumford and Shah model”, Int. J. Comp. Vis., vol. 50, no. 3, pp. 271-293, 2002.
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[2.12] S. Osher and R. P. Fedkiw, “Level set methods and dynamic implicit surfaces”, New York, USA: Springer-Verlag, 2003.
[2.13] T. Sebastian, H. Tek, J. Crisco, and B. Kimia, “Segmentation of carpal bones from CT images using skeletally coupled deformable models”, Medical Image Analysis, vol. 7, pp. 21-45, 2003.
[2.14] C. S. Poon and M. Braun, “Image segmentation by a deformable contour model incorporating region analysis”, Phys. Med. Biol., vol. 42, pp. 1833-1841, 1997.
[2.15] X. M. Pardo, M. J. Carreira, A. Mosquera, and D. Cabello, “A snake for CT image segmentation integrating region and edge information”, Image Vis. Comput., vol. 19, pp. 461-475, 2001.
[2.16] H. Tek and B. Kimia, “Volumetric segmentation of medical images by three-dimensional bubbles”, Computer Vision and Image Understanding, vol. 64, pp. 246-258, 1997.
[2.17] S. Zhu and A. Yuille, “Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation”, IEEE Trans. Patt. Anal. Mach. Intell., vol. 18, pp. 884-900, 1996.
[2.18] R. Malladi, J. A. Sethian, and B. C. Vemuri, “Shape modeling with front propagation: a level set approach”, IEEE Trans. Patt. Anal. Mach. Intell., vol. 17, no. 2, pp. 158-175, 1995.
[2.19] L. Ballerini and L. Bocchi, “Multiple genetic snakes for bone segmentation”, in Applications of Evolutionary Computing: EvoWorkshops, ser. LNCS, vol. 2611, 2003, pp. 346-356.
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[2.24] D. Mumford and J. Shah, “Optimal approximation by piecewise smooth functions and associated variational problems”, Commun. Pure Appl. Math., vol. 42, pp. 577-685, 1989. |
3. Visual tracking
[3.1] G. Welch and E. Foxlin, “Motion tracking: No silver bullet, but a respectable arsenal,” IEEE Comput. Graph. Appl., vol. 22, no. 6, pp. 24–38, 2002.
[3.2] S. Julier and G. Bishop, “Tracking: How hard can it be?,” IEEE Comput. Graph. Appl., vol. 22, no. 6, pp. 22–23, 2002.
[3.3] D. J. Kriegman, G. D. Hager, and A. S. Morse, Eds.,” The Confluence of Vision and Control”, New York: Springer-Verlag, 1998, vol. 237, Lecture Notes in Control and Information Sciences.
[3.4] I. J. Cox, “A review of statistical data association techniques for motion correspondence,” Int. J. Comput. Vision, vol. 10, no. 1, pp. 53–66, 1993.
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[3.7] C. Kublbeck and A. Ernst, “Face detection and tracking in video sequences using the modified census transformation”, Image and Vision Computing, vol. 24, pp. 564–572, 2006.
[3.8] Y. Hsiao, C. Chuang, Y. Lu, and J. Jiang, “Robust multiple objects tracking using image segmentation and trajectory estimation scheme in video frames”, Image Vis. Comput., 2006.
[3.9] D. Jackson, J. Yezzi, and S. Soatto, “Tracking Deformable Moving Objects Under Severe Occlusions”, IEEE Conf. on Decision and Control, vol.3, pp. 2990- 2995, 2004.
[3.10] C. Kambhamettua, D. Goldgofb, M. Hec, and P. Laskov, “3D nonrigid motion analysis under small deformations”, Image Vis. Comput., vol. 21, pp. 229–245, 2003.
[3.11] T. Lam and R. Lee, “Visual Tracking by Using Kalman Gradient Vector Flow (KGVF) Snakes”, Proceedings of International Conference Knowledge-Based Intelligent Information and Engineering Systems, pp. 557-563, September 2004.
[3.12] A. Blake and M. Isard, “Active Contours”, Springer Verlag, 1998.
[3.13] I. Dydenko, F. Jamal, O. Bernard, J. Dfihooge, I. E. Magnin, and D. Friboulet, “A level set framework with a shape and motion prior for segmentation and region tracking in echocardiography”, Medical Image Analysis, vol. 10, pp. 162–177, 2006.
[3.14] S. Dambreville, Y. Rathi, and A. Tannenbaum, “Tracking Deformable Objects with Unscented Kalman Filtering and Geometric Active Contours”, Proceedings of the 2006 American Control Conference Minneapolis, Minnesota, USA, June, 2006.
[3.15] M. Niethammer, A. Tannenbaum, and S. Angenent, “Dynamic Active Contours for Visual Tracking”, IEEE Trans. Automatic Control, vol. 51, no. 4, April 2006.
[3.16] N. Paragios and R. Deriche, “Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects”, IEEE Trans. Patt. Anal. Mach. Intell., vol. 22, no. 3, March 2000.
[3.17] Y. Rathi, N. Vaswani, A. Tannenbaum, and A. Yezzi, “Particle Filtering for Geometric Active Contours with Application to Tracking Moving and Deforming Objects”, IEEE Conf. on CVPR, vol. 2, pp. 2-9, 2005. |
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06-24-2008
New website launched by Activity Recognition team...
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