Tracking with kalman snakes. Active vision, pp.3-20, 1993. ,
Condensation ? conditional density propagation for visual tracking, International Journal of Computer Vision, vol.29, issue.1, pp.5-28, 1998. ,
DOI : 10.1023/A:1008078328650
A probabilistic exclusion principle for tracking multiple objects, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.57-71, 2000. ,
DOI : 10.1109/ICCV.1999.791275
Pfinder: real-time tracking of the human body, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.7, pp.780-785, 1997. ,
DOI : 10.1109/34.598236
Object contour tracking using graph cuts based active contours, Proceedings. International Conference on Image Processing, 2002. ,
DOI : 10.1109/ICIP.2002.1038959
A silhouette based human motion tracking system, pp.1178-3581, 2005. ,
Determining optical flow, Artificial Intelligence, vol.17, issue.1-3, pp.185-203, 1981. ,
DOI : 10.1016/0004-3702(81)90024-2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.185.1651
Performance of optical flow techniques, International Journal of Computer Vision, vol.54, issue.1, pp.43-77, 1994. ,
DOI : 10.1007/BF01420984
Multiple motion segmentation with level sets, IEEE Transactions on Image Processing, vol.12, issue.2, pp.201-220, 2003. ,
DOI : 10.1109/TIP.2002.807582
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.31.7864
Determining 3D motion and structure from optical flow generated by several moving objects, IEEE Trans. Pattern Anal. Mach. Intell, vol.7, issue.4, pp.384-401, 1985. ,
DOI : 10.1109/tpami.1985.4767678
Object tracking, ACM Computing Surveys, vol.38, issue.4, p.13, 2006. ,
DOI : 10.1145/1177352.1177355
Resolving motion correspondence for densely moving points, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.1, pp.54-72, 2001. ,
DOI : 10.1109/34.899946
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.105.3671
Probabilistic object tracking using multiple features, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., pp.184-187, 2004. ,
DOI : 10.1109/ICPR.2004.1334091
Kernel-based object tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.5, pp.564-575, 2003. ,
DOI : 10.1109/TPAMI.2003.1195991
Fast approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.11, pp.1222-1239, 2001. ,
DOI : 10.1109/34.969114
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.6806
Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 2001. ,
DOI : 10.1109/ICCV.2001.937505
Interactive ROI Segmentation using Graph Cuts, GVIP-ICGST Journal, vol.9, issue.6, pp.1-6, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00813390
Exact maximum a posteriori estimation for binary images, Journal of the Royal Statistical Society, Series B, vol.51, issue.2, pp.271-279, 1989. ,
Graphcut textures: image and video synthesis using graph cuts, Proceedings of SIGGRAPH, 2003. ,
DOI : 10.1145/1201775.882264
What Energy Functions Can Be Minimized via Graph Cuts, IEEE transactions on pattern analysis and machine intelligence, 2004. ,
DOI : 10.1109/tpami.2004.1262177
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.113.1823
A new approach to the maximum-flow problem, Journal of the ACM, vol.35, issue.4, pp.921-940, 1988. ,
DOI : 10.1145/48014.61051
An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision, 2000. ,
Robust Multiresolution Estimation of Parametric Motion Models, Journal of Visual Communication and Image Representation, vol.6, issue.4, pp.348-365, 1995. ,
DOI : 10.1006/jvci.1995.1029