Markovian framework for foreground-background-shadow separation of real world video scenes

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3851 LNCS, Issue , 2006, Pages 898-907

  Benedek, Csaba ^a   Szirányi, Tamás ^b  

^a PÁZMÁNY PÉTER CATHOLIC UNIVERSITY   (Hungary)

^b INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

Author keywords

[No Author keywords available]

Indexed keywords

COLOR; FEATURE EXTRACTION; MARKOV PROCESSES; MATHEMATICAL MODELS; OBJECT RECOGNITION; STATISTICAL METHODS;

BACK-GROUND-SHADOW SEPARATION; MARKOVIAN FRAMEWORKS; REAL WORLD VIDEO SCENES; TEMPORAL STATISTICS;

IMAGE PROCESSING;

EID: 33744954909     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11612032_90     Document Type: Conference Paper

References (15)

1. Cs. Benedek, T. Szirányi: A Markov Random Field Model for Foreground-Background Separation, Joint Hungarian-Austrian Conference on Image Processing and Pattern Recognition (HACIPPR), Veszprém, Hungary, May 11-13, (2005)
2. M. Berthod, Z. Kato, S. Yu, J. Zerubia: Bayesian image classification using Markov Random Fields. Image and Vision Computing 14 (1996) 285-295
3. R. Cucchiara, C. Grana, G. Neri, M. Piccardi, and A. Prati: The Sakbot System for Moving Object Detection and Tracking. Video-Based Surveillance Systems-Computer Vision and Distributed Processing (2001) 145-157
4. L. Czúni, T. Szirányi: Motion Segmentation and Tracking with Edge Relaxation and Optimization using Fully Parallel Methods in the Cellular Nonlinear Network Architecture. Real-Time Imaging Vol.7, No.1, (2001) 77-95
5. S. Geman and D. Geman: Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence (1984) 721-741
6. I. Mikic, P. Cosman, G. Kogut and M. M. Trivedi: Moving Shadow and Object Detection in Traffic Scenes, Proc. ICPR, (2000) 321-324
7. N. Paragios, V. Ramesh. A MRF-based Real-Time Approach for Subway Monitoring. In IEEE Conference in Computer Vision and Pattern Recognition (CVPR), (2001) 1034-1040
8. A. Prati, I. Mikic, M. M. Trivedi, R. Cucchiara: Detecting moving shadows: algorithms and evaluation. PAMI(25), (2003) 7, pp. 918-923
9. J. Rittscher, J. Kato, S. Joga and A. Blake: A Probabilistic Background Model for Tracking Proc. European Conf. Computer (2000)
10. C. Stauffer and W. E. L. Crimson: Learning Patterns of Activity Using Real-Time Tracking, IEEE Trans. Pattern Anal. Mach. Intell. (2000) 22(8): 747-757
11. T. Szirányi, J. Zerubia: Markov Random Field Image Segmentation using Cellular Neural Network , IEEE Tr. Circuits and Systems (1997) I., V.44, pp.86-89,
12. A. Yilmaz, X. Li, M. Shah Object Contour Tracking Using Level Sets. Asian Conference on Computer Vision, ACCV 2004, Jaju Islands, Korea, (2004)
13. P. Viola, M. Jones: Rapid Object Detection Using a Boosted Cascade of Simple Features, Proc. IEEE Conf. Computer Vision and Pattern Recognition, (2001)
14. Y. Wang, T. Tan, and K.-F. Loe:A Dynamic Hidden Markov Random Field Model for Foreground and Shadow Segmentation Seventh IEEE Workshops on Application of Computer Vision, Breckenridge, Colorado, (2005)
15. Yue Zhou, Yihong Gong, and Hai Tao: Background segmentation using spatial-temporal multi-resolution MRF, IEEE Motion05, (January 2005)