Background segmentation using spatial-temporal multi-resolution MRF

Proceedings - IEEE Workshop on Motion and Video Computing, MOTION 2005

Volumn , Issue , 2005, Pages 8-13

  Yue, Zhou ^a   Wei, Xu ^b   Hai, Tao ^c   Yihong, Gong ^b  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b NEC LABORATORIES AMERICA   (United States)

^c University of California Santa Cruz   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; IMAGE COMPRESSION; IMAGE RECONSTRUCTION; MARKOV PROCESSES; PIXELS; REAL TIME SYSTEMS; ROBUST CONTROL; THREE DIMENSIONAL;

BACKGROUND SEGMENTATION; MARKOV RANDOM FIELD (MRF); MAXIMUM A POSTERIOR (MAP);

IMAGE SEGMENTATION;

EID: 35348849048     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACVMOT.2005.32     Document Type: Conference Paper

References (17)

1. A. P. Dempster, N.M. Laird, and D.B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of Royal Statistical Society, 39(1):138, 1977
2. A. Elgammal, D. Harwood, and L. Davis. Nonparametric model for background subtraction, Proc. IEEE Frame Rate Workshop, 1999
3. Gelman, A., Carlin, J.B., Stem, H.S. & Rubin, D.B. (1995). Bayesian Data Analysis, Chapman & Hall, London.
4. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 6, No. 6, pp. 721-741, 1984.
5. W.E.L. Grimson, C. Stauffer, R. Romano, and L. Lee, "Using adaptive tracking to classify and monitor activities in a site," In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), pp. 22-9, 1998.
6. I. Haritaoglu, D. Harwood, and L.S. Davis, W4: Real-time surveillance of people and their activities. IEEE Transaction on Pattern Analysis and Machine Intelligence, 22(8):809-830, 2000.
7. T. Horprasert, D. Harwood, and L.S. Davis, "A Statistical Approach for Real-time Robust Background Subtraction and Shadow Detection", Proc. IEEE ICCV99 FRAME-RATE Workshop, Kerkyra, Greece, September 1999
8. S. Z. Li. Markov Random Field Modeling in Image Analysis. Springer-Verlag, 2001
9. M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul. An introduction to variational methods for graphical models. In M. I. Jordan (Ed.), Learning in Graphical Models, Cambridge: MIT Press, 1999.
10. Murphy K., Weiss Y. and Jordan M., Loopy belief propagation for approximate inference: an empirical study. In Laskey K.B. and Prade H. (editors) Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI), 1999.
11. N. M. Oliver, B. Rosario, and A. Pentland. A Bayesian computer vision system for modeling human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8): 831-843, 2000.
12. Lawrence R. Rabiner. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 77(2):257-286, 1989.
13. K. Rittscher, J. Kato, S. Joga, and A. Blake "A probabilistic background model for tracking," in Proc. 6th Eur. Conf. Computer Vision, vol. 2, 2000, pp. 336-350.
14. C. Stauffer and W.E.L. Grimson. Adaptive background mixture models for real-time tracking. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), pp II: 246-252, 1999.
15. B. Stenger, V. Ramesh, N. Paragios, F. Coetzee, and J. Bouhman, "Topology free hidden markov models: Application to background modeling," in Proc. IEEE Int. Conf. Computer Vision, 2001, pp. 294-301.
16. K. Toyarea, J. Krumm, B. Brumitt, and B. Meyers, Wallflower: Principles and practice of background maintenance," in International Conference on Computer Vision, 1999, pp. 255-261.
17. Wren, Christopher R., Ali Azarbayejani, Trevor Darrell, and Alex Pentland "Pfinder: real-time tracking of the human body," in IEEE Transaction on Pattern Analysis and Machine Intelligence, July 1997, vol 19, no 7, pp 780-785.[18] Yedidia J.S., Freeman W.T. and Weiss Y, Understanding Belief Propagation and Its Generalizations, IJCAI 2001 Distinguished Lecture track