D. Adelseinsson and J. Sethian, A Fast Level Set Method for Propagating Interfaces, Journal of Computational Physics, vol.118, issue.2, pp.269-277, 1995.
DOI : 10.1006/jcph.1995.1098

F. Andreu, C. Ballester, V. Caselles, and J. M. Mazón, Minimizing total variation flow, Comptes Rendus de l'Acad??mie des Sciences - Series I - Mathematics, vol.331, issue.11, pp.321-360, 2001.
DOI : 10.1016/S0764-4442(00)01729-8

J. L. Barron, D. J. Fleet, and S. S. Beauchemin, Performance of optical flow techniques, International Journal of Computer Vision, vol.54, issue.1, pp.43-77, 1994.
DOI : 10.1007/BF01420984

S. Belongie, C. Carson, H. G. Nad, and J. Malik, Color-and texture-based image segmentation using the expectation-maximization algorithm and its application to content-based image retrieval, Sixth International Conference on Computer Vision, pp.675-682, 1998.

J. Bigün, G. H. Granlund, and J. Wiklund, Multidimensional orientation estimation with applications to texture analysis and optical flow, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, issue.8, pp.775-790, 1991.
DOI : 10.1109/34.85668

P. Brodatz, Textures: a Photographic Album for Artists and Designers, 1966.

T. Brox and J. Weickert, Nonlinear Matrix Diffusion for Optic Flow Estimation, Pattern Recognition Lecture Notes in Computer Science, vol.2449, pp.446-453, 2002.
DOI : 10.1007/3-540-45783-6_54

T. Brox, M. Welk, G. Steidl, and J. Weickert, Equivalence Results for TV Diffusion and TV Regularisation, Scale-Space Theories in Computer Vision, 2003.
DOI : 10.1007/3-540-44935-3_7

T. Chan, B. Sandberg, and L. Vese, Active Contours without Edges for Vector-Valued Images, Journal of Visual Communication and Image Representation, vol.11, issue.2, pp.130-141, 2000.
DOI : 10.1006/jvci.1999.0442
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.6580

T. F. Chan and L. A. Vese, A level set algorithm for minimizing the Mumford-Shah functional in image processing, Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision, pp.161-168, 2001.
DOI : 10.1109/VLSM.2001.938895

Y. Chen, H. D. Tagare, S. Thiruvenkadam, F. Huang, K. S. Gopinath et al., Using prior shapes in geometric active contours in a variational framework, International Journal of Computer Vision, vol.50, issue.3, pp.315-328, 2002.
DOI : 10.1023/A:1020878408985

D. Chopp, Computing Minimal Surfaces via Level Set Curvature Flow, Journal of Computational Physics, vol.106, issue.1, pp.77-91, 1993.
DOI : 10.1006/jcph.1993.1092

D. Cremers, F. Tischhäuser, J. Weickert, and C. Schnörr, Diffusion snakes: introducing statistical shape knowledge into the Mumford-Shah functional, International Journal of Computer Vision, vol.50, issue.3, pp.295-313, 2002.
DOI : 10.1023/A:1020826424915

G. R. Cross and A. K. Jain, Markov Random Field Texture Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.5, issue.1, pp.25-39, 1983.
DOI : 10.1109/TPAMI.1983.4767341

D. Zenzo, A note on the gradient of a multi-image. Computer Vision, Graphics and Image Processing, pp.116-125, 1986.

F. Dibos and G. Koepfler, Global Total Variation Minimization, SIAM Journal on Numerical Analysis, vol.37, issue.2, pp.646-664, 2000.
DOI : 10.1137/S0036142998334838

W. Förstner and E. Gülch, A fast operator for detection and precise location of distinct points, corners and centres of circular features, Proc. ISPRS Intercommission Conference on Fast Processing of Photogrammetric Data, pp.281-305, 1987.

D. Gabor, Theory of communication, Journal of the Institution of Electrical Engineers - Part I: General, vol.94, issue.73, pp.429-459, 1946.
DOI : 10.1049/ji-1.1947.0015

G. Gerig, O. Kübler, R. Kikinis, and F. A. Jolesz, Nonlinear anisotropic filtering of MRI data, IEEE Transactions on Medical Imaging, vol.11, issue.2, pp.221-232, 1992.
DOI : 10.1109/42.141646

G. H. Granlund and H. Knutsson, Signal Processing for Computer Vision, 1995.
DOI : 10.1007/978-1-4757-2377-9

T. Hansen and H. Neumann, Neural Mechanisms for Representing Surface and Contour Features, Emergent Neural Computational Architectures Based on Neuroscience, pp.139-153, 2001.
DOI : 10.1007/3-540-44597-8_10

B. Horn and B. Schunck, Determining optical flow, Artificial Intelligence, vol.17, issue.1-3, pp.185-203, 1981.
DOI : 10.1016/0004-3702(81)90024-2

S. Jehan-besson, M. Barlaud, and G. Aubert, DREAM2S: Deformable Regions Driven by an Eulerian Accurate Minimization Method for Image and Video Segmentation, Computer Vision ? ECCV 2002, pp.365-380, 2002.
DOI : 10.1007/3-540-47977-5_24
URL : https://hal.archives-ouvertes.fr/hal-00664596

S. Keeling and R. Stollberger, Nonlinear anisotropic diffusion filtering for multiscale edge enhancement, Inverse Problems, vol.18, issue.1, pp.175-190, 2002.
DOI : 10.1088/0266-5611/18/1/312

J. Kim, J. Fisher, A. Yezzi, M. Cetin, and A. Willsky, Nonparametric methods for image segmentation using information theory and curve evolution, IEEE International Conference on Image Processing, 2002.

D. V. Kovkov and V. I. Tsurkov, Singular solutions to the problem with initial conditions for the equation of anisotropic diffusion, Journal of Computer and Systems Sciences International, vol.39, issue.4, pp.497-502, 2000.

M. E. Leventon, W. E. Grimson, and O. Faugeras, Statistical shape infuence in geodesic active contours, Proc. 2000 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.316-323, 2000.

T. Lindeberg, Scale-Space Theory in Computer Vision, 1994.
DOI : 10.1007/978-1-4757-6465-9
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.3577

B. Lucas and T. Kanade, An iterative image registration technique with an application to stereo vision, Proc. Seventh International Joint Conference on Artificial Intelligence, pp.674-679, 1981.

J. Malik, S. Belongie, T. Leung, and J. Shi, Contour and Texture Analysis for Image Segmentation, International Journal of Computer Vision, vol.43, issue.1, pp.7-27, 2001.
DOI : 10.1007/978-1-4615-4413-5_9

R. Malladi, J. Sethian, and B. Vermuri, Shape modeling with front propagation: a level set approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.17, issue.2, pp.158-175, 1995.
DOI : 10.1109/34.368173

S. Marcelja, Mathematical description of the responses of simple cortical cells*, Journal of the Optical Society of America, vol.70, issue.11, pp.1297-1300, 1980.
DOI : 10.1364/JOSA.70.001297

M. Middendorf and H. Nagel, Empirically Convergent Adaptive Estimation of Grayvalue Structure Tensors, Pattern Recognition Lecture Notes in Computer Science, vol.2449, pp.66-74, 2002.
DOI : 10.1007/3-540-45783-6_9

E. Mingolla, W. Ross, and S. Grossberg, A neural network for enhancing boundaries and surfaces in synthetic aperture radar images, Neural Networks, vol.12, issue.3, pp.499-511, 1999.
DOI : 10.1016/S0893-6080(98)00144-0

A. Mitiche and P. Bouthemy, Computation and analysis of image motion: A synopsis of current problems and methods, International Journal of Computer Vision, vol.7, issue.4, pp.29-55, 1996.
DOI : 10.1007/BF00131147

H. Neumann, L. Pessoa, and T. Hansen, Visual filling-in for computing perceptual surface properties, Biological Cybernetics, vol.85, issue.5, pp.355-369, 2001.
DOI : 10.1007/s004220100258

S. Osher and J. A. Sethian, Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics, vol.79, issue.1, pp.12-49, 1988.
DOI : 10.1016/0021-9991(88)90002-2

N. Paragios and R. Deriche, Geodesic active contours and level sets for the detection and tracking of moving objects, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.3, pp.266-280, 2000.
DOI : 10.1109/34.841758

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, p.223, 2002.
DOI : 10.1023/A:1014080923068

N. Paragios and D. R. , Geodesic Active Regions: A New Framework to Deal with Frame Partition Problems in Computer Vision, Journal of Visual Communication and Image Representation, vol.13, issue.1-2, pp.249-268, 2002.
DOI : 10.1006/jvci.2001.0475

D. Peng, B. Merriman, S. Osher, H. Zhao, and M. Kang, A PDE-Based Fast Local Level Set Method, Journal of Computational Physics, vol.155, issue.2, pp.410-438, 1999.
DOI : 10.1006/jcph.1999.6345

P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.7, pp.629-639, 1990.
DOI : 10.1109/34.56205

C. Peterson and B. Söderberg, A NEW METHOD FOR MAPPING OPTIMIZATION PROBLEMS ONTO NEURAL NETWORKS, International Journal of Neural Systems, vol.01, issue.01, pp.3-22, 1989.
DOI : 10.1142/S0129065789000414

J. Puzicha, T. Hofmann, and J. Buhmann, Deterministic annealing: fast physical heuristics for real-time optimization of large systems, Proc. 15th IMACS World Conference on Scientific Computation, Modelling and Applied Mathematics, 1997.

A. R. Rao and B. G. Schunck, Computing oriented texture fields. CVGIP: Graphical Models and Image Processing, pp.157-185, 1991.

M. Rousson, T. Brox, and R. Deriche, Active unsupervised texture segmentation on a diffusion based feature space, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., 2003.
DOI : 10.1109/CVPR.2003.1211535
URL : https://hal.archives-ouvertes.fr/inria-00071891

M. Rousson and R. Deriche, A variational framework for active and adaptive segmentation of vector valued images, Proc. IEEE Workshop on Motion and Video Computing, pp.56-61, 2002.

M. Rousson and N. Paragios, Shape Priors for Level Set Representations, Computer Vision ? ECCV 2002, pp.78-92, 2002.
DOI : 10.1007/3-540-47967-8_6

L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, vol.60, issue.1-4, pp.259-268, 1992.
DOI : 10.1016/0167-2789(92)90242-F

C. Sagiv, N. A. Sochen, and Y. Y. Zeevi, Texture segmentation via a diffusion-segmentation scheme in the gabor feature space, Proc. Texture 2002, 2nd International Workshop on Texture Analysis and Synthesis, 2002.

B. Sandberg, T. Chan, and L. Vese, A level-set and Gabor-based active contour algorithm for segmenting textured images, 2002.

C. Stiller and J. Konrad, Estimating motion in image sequences, IEEE Signal Processing Magazine, vol.16, issue.4, pp.70-91, 1999.
DOI : 10.1109/79.774934

D. Tschumperlé and R. Deriche, Diffusion tensor regularization with contraints preservation, Proc. 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.948-953, 2001.

D. Tschumperlé and R. Deriche, Orthonormal vector sets regularization with PDE's and applications, International Journal of Computer Vision, vol.50, issue.3, pp.237-252, 2002.
DOI : 10.1023/A:1020870207168

V. I. Tsurkov, An analytical model of edge protection under noise suppression by anisotropic diffusion, Journal of Computer and Systems Sciences International, vol.39, issue.3, pp.437-440, 2000.

J. Weickert, A review of nonlinear diffusion filtering, Scale-Space Theory in Computer Vision, pp.3-28, 1997.
DOI : 10.1007/3-540-63167-4_37

J. Weickert and B. Benhamouda, A semidiscrete nonlinear scale-space theory and its relation to the Perona???Malik paradox, Advances in Computer Vision, pp.1-10, 1997.
DOI : 10.1007/978-3-7091-6867-7_1

J. Weickert and T. Brox, Diffusion and regularization of vector- and matrix-valued images, Inverse Problems, pp.251-268, 2002.
DOI : 10.1090/conm/313/05378

J. Weickert, B. M. Ter-haar-romeny, and M. A. Viergever, Efficient and reliable schemes for nonlinear diffusion filtering, IEEE Transactions on Image Processing, vol.7, issue.3, pp.398-410, 1998.
DOI : 10.1109/83.661190

A. Yezzi, A. Tsai, and A. Willsky, A statistical approach to snakes for bimodal and trimodal imagery, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.898-903, 1999.
DOI : 10.1109/ICCV.1999.790317

H. Zhao, T. Chan, B. Merriman, and S. Osher, A Variational Level Set Approach to Multiphase Motion, Journal of Computational Physics, vol.127, issue.1, pp.179-195, 1996.
DOI : 10.1006/jcph.1996.0167

S. Zhu and A. Yuille, Region competition: unifying snakes, region growing, energy/Bayes/MDL for multi-band image segmentation, Proceedings of IEEE International Conference on Computer Vision, pp.884-900, 1996.
DOI : 10.1109/ICCV.1995.466909

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