C. Benedek and T. Sziranyi, Bayesian Foreground and Shadow Detection in Uncertain Frame Rate Surveillance Videos, IEEE Transactions on Image Processing, vol.17, issue.4, pp.608-621, 2008.
DOI : 10.1109/TIP.2008.916989

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, pp.1222-1239, 2001.
DOI : 10.1109/iccv.1999.791245

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, Detecting moving objects, ghosts, and shadows in video streams, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.10, pp.251337-1342, 2003.
DOI : 10.1109/TPAMI.2003.1233909

G. S. Fung, N. H. Yung, G. K. Pang, and A. H. Lai, Effective moving cast shadow detection for monocular color traffic image sequences, Optical Engineering, vol.41, issue.6, pp.411425-1440, 2002.
DOI : 10.1117/1.1473638

S. Geman and D. Geman, Stochastic relaxation, gibbs distributions , and the bayesian restoration of images, pp.721-741, 1984.

T. Horprasert, D. Harwood, and L. S. Davis, A statistical approach for real-time robust background subtraction and shadow detection, Proc. ICCV Frame-rate Workshop, 1999.

D. S. Lee, Effective gaussian mixture learning for video background subtraction, PAMI, vol.27, issue.5, pp.827-832, 2005.

Z. Liu, K. Huang, T. Tan, and L. Wang, Cast Shadow Removal Combining Local and Global Features, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
DOI : 10.1109/CVPR.2007.383510

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

N. Martel-brisson and A. Zaccarin, Learning and Removing Cast Shadows through a Multidistribution Approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.7, pp.1133-1146, 2007.
DOI : 10.1109/TPAMI.2007.1039

S. Nadimi and B. Bhanu, Physical models for moving shadow and object detection in video, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.8, pp.1079-1087, 2004.
DOI : 10.1109/TPAMI.2004.51

F. Porikli and J. Thornton, Shadow flow: a recursive method to learn moving cast shadows, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.891-898, 2005.
DOI : 10.1109/ICCV.2005.217

R. Potts, Some generalized order-disorder transformations, Proc. of the Cambridge Philosoph. Soc, p.81, 1952.
DOI : 10.1103/PhysRev.60.252

A. Prati, I. Mikic, M. Trivedi, and R. Cucchiara, Detecting moving shadows: algorithms and evaluation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.7, pp.918-923, 2003.
DOI : 10.1109/TPAMI.2003.1206520

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

S. R. Sain, Multivariate locally adaptive density estimation, Computational Statistics & Data Analysis, vol.39, issue.2, pp.165-186, 2002.
DOI : 10.1016/S0167-9473(01)00053-6

O. Schreer, I. Feldmann, U. Golz, and P. A. Kauff, Fast and robust shadow detection in videoconference applications, International Symposium on VIPromCom Video/Image Processing and Multimedia Communications, pp.371-375, 2002.
DOI : 10.1109/VIPROM.2002.1026685

Y. Sheikh and M. Shah, Bayesian modeling of dynamic scenes for object detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.11, pp.1778-1792, 2005.
DOI : 10.1109/TPAMI.2005.213

C. Stauffer and W. E. Grimson, Adaptive background mixture models for real-time tracking, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), p.252, 1999.
DOI : 10.1109/CVPR.1999.784637

M. Wand and M. Jones, Kernel Smoothing, 1995.
DOI : 10.1007/978-1-4899-4493-1

Y. Wang, K. Loe, and J. Wu, A dynamic conditional random field model for foreground and shadow segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.2, pp.279-289, 2006.
DOI : 10.1109/TPAMI.2006.25

Z. Wei, F. X. Zhong, X. K. Yang, and Q. M. Wu, Moving cast shadows detection using ratio edge, IEEE Trans. Multimedia, vol.9, issue.6, pp.1202-1214, 2007.