I. A. Ahmad and P. E. Lin, A nonparametric estimation of the entropy for absolutely continuous distributions (Corresp.), IEEE Transactions on Information Theory, vol.22, issue.3, pp.372-375, 1976.
DOI : 10.1109/TIT.1976.1055550

G. Aubert, M. Barlaud, O. Faugeras, and S. Jehan-besson, Image Segmentation Using Active Contours: Calculus of Variations or Shape Gradients?, SIAM Journal on Applied Mathematics, vol.63, issue.6, pp.2128-2154, 2003.
DOI : 10.1137/S0036139902408928

URL : https://hal.archives-ouvertes.fr/inria-00072105

R. Venkatesh-babu, P. Pérez, and P. Bouthemy, Robust tracking with motion estimation and local Kernel-based color modeling, Image and Vision Computing, vol.25, issue.8, pp.1205-1216, 2007.
DOI : 10.1016/j.imavis.2006.07.016

A. Banerjee, S. Merugu, I. Dhillon, and J. Ghosh, Clustering with Bregman Divergences, J. Mach. Learn. Res, vol.6, pp.1705-1749, 2005.
DOI : 10.1137/1.9781611972740.22

M. J. Black and P. Anandan, The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields, Computer Vision and Image Understanding, vol.63, issue.1, pp.75-104, 1996.
DOI : 10.1006/cviu.1996.0006

S. Boltz, ´. E. Debreuve, and M. Barlaud, Highdimensional statistical distance for region-ofinterest tracking: Application to combining a soft geometric constraint with radiometry, International Conference on Computer Vision and Pattern Recognition, 2007.

S. Boltz, A. Herbulot, ´. E. Debreuve, M. Barlaud, and G. Aubert, Motion and Appearance Nonparametric Joint Entropy for??Video??Segmentation, International Journal of Computer Vision, vol.9, issue.2
DOI : 10.1007/s11263-007-0124-2

URL : https://hal.archives-ouvertes.fr/hal-00329748

S. Boltz, ´. E. Wolsztynski, ´. E. Debreuve, ´. E. Thierry, M. Barlaud et al., A minimumentropy procedure for robust motion estimation, International Conference on Image Processing, 2006.

T. Brox, A. Bruhn, N. Papenberg, and J. Weickert, High Accuracy Optical Flow Estimation Based on a Theory for Warping, European Conference on Computer Vision, 2004.
DOI : 10.1007/978-3-540-24673-2_3

T. Brox, M. Rousson, R. Deriche, and J. Weickert, Unsupervised Segmentation Incorporating Colour, Texture, and Motion, Computer Analysis of Images and Patterns, 2003.
DOI : 10.1007/978-3-540-45179-2_44

URL : https://hal.archives-ouvertes.fr/inria-00071826

A. Buades, B. Coll, and J. Morel, A Non-Local Algorithm for Image Denoising, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005.
DOI : 10.1109/CVPR.2005.38

A. Buades, B. Coll, and J. Morel, A Review of Image Denoising Algorithms, with a New One, Multiscale Modeling & Simulation, vol.4, issue.2, pp.490-530, 2005.
DOI : 10.1137/040616024

URL : https://hal.archives-ouvertes.fr/hal-00271141

A. Bugeau and P. Pérez, Detection and segmentation of moving objects in highly dynamic scenes, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383244

URL : https://hal.archives-ouvertes.fr/hal-00551596

G. Carlsson, T. Ishkhanov, V. De-silva, and A. Zomorodian, On the Local Behavior of Spaces of Natural Images, International Journal of Computer Vision, vol.265, issue.4, pp.1-12, 2008.
DOI : 10.1007/s11263-007-0056-x

R. Collins, X. Zhou, and S. K. Teh, An open source tracking testbed and evaluation web site, IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), Breckenridge (CO), USA, 2005. Code available at

]. D. Comaniciu, V. Ramesh, and P. Meer, Real-time tracking of non-rigid objects using mean shift, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), 2000.
DOI : 10.1109/CVPR.2000.854761

D. Comaniciu, An algorithm for data-driven bandwidth selection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.2, pp.281-288, 2003.
DOI : 10.1109/TPAMI.2003.1177159

J. Costa and A. O. Hero, Manifold learning using euclidean K-nearest neighbor graphs, International Conference on Acoustics, Speech, and Signal Processing, 2004.

D. Cremers, M. Rousson, and R. Deriche, A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape, International Journal of Computer Vision, vol.18, issue.9, pp.195-215, 2007.
DOI : 10.1007/s11263-006-8711-1

A. Elgammal, R. Duraiswami, and L. S. Davis, Probabilistic tracking in joint feature-spatial spaces, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., 2003.
DOI : 10.1109/CVPR.2003.1211432

E. Fix and J. L. Hodges, Discriminatory analysis , non-parametric discrimination: consistency properties, 1951.

K. Fukunaga, Introduction to statistical pattern recognition, 1990.

K. Fukunaga and L. D. Hostetler, The estimation of the gradient of a density function, with applications in pattern recognition, IEEE Transactions on Information Theory, vol.21, issue.1, pp.32-40, 1975.
DOI : 10.1109/TIT.1975.1055330

B. Georgescu, I. Shimshoni, and P. Meer, Mean shift based clustering in high dimensions: a texture classification example, Proceedings Ninth IEEE International Conference on Computer Vision, 2003.
DOI : 10.1109/ICCV.2003.1238382

M. N. Goria, N. N. Leonenko, V. V. Mergel, P. L. Novi, and . Inverardi, A new class of random vector entropy estimators and its applications in testing statistical hypotheses, 27] DPI Göttingen, TSTOOL toolbox for nearest neighbor statistics, pp.277-297, 1997.
DOI : 10.2307/2285889

A. Herbulot, S. Jehan-besson, S. Duffner, M. Barlaud, and G. Aubert, Segmentation of Vectorial Image Features Using Shape Gradients and Information Measures, Journal of Mathematical Imaging and Vision, vol.18, issue.1, pp.365-386, 2006.
DOI : 10.1007/s10851-006-6898-y

URL : https://hal.archives-ouvertes.fr/hal-00418217

A. Ihler, Kernel density estimation toolbox for Matlab Code available at, 2003.

J. Kim, J. W. Fisher, A. Yezzi, M. , and A. S. Willsky, Nonparametric methods for image segmentation using information theory and curve evolution, IEEE Trans. Image Process, vol.14, issue.10, pp.1486-1502, 2005.

L. Kozachenko and N. Leonenko, On statistical estimation of entropy of random vector, Problems Infor. Transmiss, vol.23, issue.23 2, pp.95-101, 1987.

A. B. Lee, K. S. Pedersen, and D. Mumford, The nonlinear statistics of high-contrast patches in natural images, International Journal of Computer Vision, vol.54, issue.1/2, pp.83-103, 2003.
DOI : 10.1023/A:1023705401078

J. Lin, Divergence measures based on the Shannon entropy, IEEE Transactions on Information Theory, vol.37, issue.1, pp.145-151, 1991.
DOI : 10.1109/18.61115

D. O. Loftsgaarden and C. P. Quesenberry, A Nonparametric Estimate of a Multivariate Density Function, The Annals of Mathematical Statistics, vol.36, issue.3, pp.1049-1051, 1965.
DOI : 10.1214/aoms/1177700079

G. David and . Lowe, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vision, vol.60, issue.2, pp.91-110, 2004.

T. Minka, Divergence measures and message passing, 2005.

N. Paragios and R. Deriche, Geodesic active regions and level set methods for supervised texture segmentation, International Journal of Computer Vision, vol.46, issue.3, pp.223-247, 2002.
DOI : 10.1023/A:1014080923068

P. Pérez, C. Hue, J. Vermaak, and M. Gangnet, Color-Based Probabilistic Tracking, European Conference on Computer Vision, 2002.
DOI : 10.1007/3-540-47969-4_44

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

D. W. Scott, Multivariate Density Estimation: Theory, Practice, and Visualization Density Estimation for Statistics and Data Analysis, 1986.

G. R. Terrell and D. W. Scott, Variable Kernel Density Estimation, The Annals of Statistics, vol.20, issue.3, pp.1236-1265, 1992.
DOI : 10.1214/aos/1176348768

P. Viola and W. M. Wells, Alignment by maximization of mutual information, Proceedings of IEEE International Conference on Computer Vision, pp.137-154, 1997.
DOI : 10.1109/ICCV.1995.466930

J. Weickert and C. Schnörr, Variational optic flow computation with a spatio-temporal smoothness constraint, Journal of Mathematical Imaging and Vision, vol.14, issue.3, pp.245-255, 2001.
DOI : 10.1023/A:1011286029287

C. Yang, R. Duraiswami, A. Nail, L. Gumerov, and . Davis, Improved fast gauss transform and efficient kernel density estimation, Proceedings Ninth IEEE International Conference on Computer Vision, 2003.
DOI : 10.1109/ICCV.2003.1238383

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.137.2987

S. Zhu and K. Ma, Correction to "A new diamond search algorithm for fast block-matching motion estimation", IEEE Transactions on Image Processing, vol.9, issue.3, pp.287-290, 2000.
DOI : 10.1109/TIP.2000.826791