?. Base-de and . Beta, 162 séquences d'images de textures dynamiques regroupées en 10 classes : Mer

?. Base-de and . Gamma, séquences d'images de textures dynamiques regroupées en 11 classes : Fleurs, Mer, Arbres sans feuillage, Feuillage dense, Escalator, Eau calme, Drapeaux, Herbes, Trafic routier, Fontaines, Feu. Dans cette base de données, les classes sont dotées de nombreuxéchantillonsnombreuxéchantillons pour couvrir de nombreux cas (changement d'´ echelles, d'orientation) Il s'agit d, p.275

S. Dubois, J. Ogier, and M. Ménard, Segmenting and Indexing Old Documents using a Letter Extraction, 8th International Workshop on Graphics Recognition, pp.22-23, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00498379

M. Dubois, R. Lugiez, M. Péteri, and . Ménard, Adding a Noise Component To A Color Decomposition Model For Improving Color Texture Extraction Sloven, 4th European Conference on Colour in Graphics, Imaging, and Vision, pp.9-13, 2008.

M. Ssl, S. , S. , S. Ssl, S. et al., Les courbes représentent le nombre de coefficients seuillés (enéchelleenéchelle logarithmique) au cours des itérations sur quatre séquences d'images différentes, p.134

T. Bibliographie, S. Amiaz, D. Fazekas, &. N. Chetverikov, and . Kiryati, Detecting Regions of Dynamic Texture, International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 07), pp.848-859, 2007.

]. J. Aujol-05a, G. Aujol, L. Aubert, &. A. Blanc-féraud, and . Chambolle, Image Decomposition into a Bounded Variation Component and an Oscillating Component, Journal of Mathematical Imaging and Vision, vol.15, issue.3, pp.71-88, 2005.
DOI : 10.1007/s10851-005-4783-8

]. J. Aujol-05b, &. A. Aujol, and . Chambolle, Dual Norms and Image Decomposition Models, International Journal of Computer Vision, vol.19, issue.3, pp.85-104, 2005.
DOI : 10.1007/s11263-005-4948-3

J. F. Aujol and &. G. Gilboa, Constrained and SNR-Based Solutions for TV-Hilbert Space Image Denoising, Rolfs & F. Yu. Video Restoration using Multichannel- Morphological Component Analysis Inpainting, pp.217-237, 2006.
DOI : 10.1007/s10851-006-7801-6

URL : https://hal.archives-ouvertes.fr/hal-00201844

R. H. Bamberger and &. M. Smith, A filter bank for the directional decomposition of images: theory and design, IEEE Transactions on Signal Processing, vol.40, issue.4, pp.882-893, 1992.
DOI : 10.1109/78.127960

R. Joseph, D. El-yaniv, &. M. Lischinski, and . Werman, Texture mixing and texture movie synthesis using statistical learning, IEEE Transactions on Visualization and Computer Graphics, vol.7, issue.2, pp.120-135, 2001.
DOI : 10.1109/2945.928165

J. Bobin, J. L. Starck, J. M. Fadili, Y. Moudden, and &. D. Donoho, Morphological Component Analysis: An Adaptive Thresholding Strategy, IEEE Transactions on Image Processing, vol.16, issue.11, pp.2675-2681, 2007.
DOI : 10.1109/TIP.2007.907073

URL : https://hal.archives-ouvertes.fr/hal-00196076

J. Bobin, J. L. Starck, Y. Moudden, and &. J. Fadili, Blind Source Separation: The Sparsity Revolution, Advances in Imaging and Electron Physics, pp.221-298, 2008.
DOI : 10.1016/S1076-5670(08)00605-8

URL : https://hal.archives-ouvertes.fr/hal-00252075

P. Bouthemy and &. Fablet, Motion characterization from temporal cooccurrences of local motion-based measures for video indexing, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), pp.905-908, 1998.
DOI : 10.1109/ICPR.1998.711298

]. E. Candès-00a, &. D. Candès, and . Donoho, Curvelets -A Surprisingly Effective Nonadaptive Representation For Objects with Edges, p.74, 2000.

]. E. Candès and &. D. Donoho, singularities, Communications on Pure and Applied Mathematics, vol.9, issue.7, pp.219-266, 2004.
DOI : 10.1002/cpa.10116

]. E. Candès-05a, &. L. Candès, and . Demanet, The curvelet representation of wave propagators is optimally sparse, Communications on Pure and Applied Mathematics, vol.1, issue.2, pp.1472-1528, 2005.
DOI : 10.1002/cpa.20078

]. E. Candès-05b, L. Candès, D. L. Demanet, &. L. Donoho, and . Ying, Fast Discrete Curvelet Transforms. Rapport technique, California Institute of Technology, pp.65-69, 2005.

]. E. Candès-05c, &. D. Candès, and . Donoho, Continuous curvelet transform, Applied and Computational Harmonic Analysis, vol.19, issue.2, pp.162-197, 2005.
DOI : 10.1016/j.acha.2005.02.003

]. E. Candès-05d, &. D. Candès, and . Donoho, Continuous curvelet transform, Applied and Computational Harmonic Analysis, vol.19, issue.2, pp.198-222, 2005.
DOI : 10.1016/j.acha.2005.02.004

S. Chan, &. J. Osher, and . Shen, The digital TV filter and nonlinear denoising, IEEE Transactions on Image Processing, vol.10, issue.2, pp.231-241, 2001.
DOI : 10.1109/83.902288

]. A. Chan-05a, &. N. Chan, and . Vasconcelos, Mixtures of dynamic textures, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.641-647, 2005.
DOI : 10.1109/ICCV.2005.151

]. A. Chan-05b, &. N. Chan, and . Vasconcelos, Probabilistic Kernels for the Classification of Auto-Regressive Visual Processes, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.846-851, 2005.
DOI : 10.1109/CVPR.2005.279

A. B. Chan and &. N. Vasconcelos, Layered Dynamic Textures, IEEE Transactions on Pattern Analysis and Machine Intelligence, p.42, 2006.
DOI : 10.1109/TPAMI.2009.110

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.113.319

A. B. Chan and &. N. Vasconcelos, Classifying Video with Kernel Dynamic Textures, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-6, 2007.
DOI : 10.1109/CVPR.2007.382996

A. B. Chan and &. N. Vasconcelos, Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.5, pp.909-926, 2008.
DOI : 10.1109/TPAMI.2007.70738

A. B. Chan and &. N. Vasconcelos, Variational layered dynamic textures, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.1062-1069, 2009.
DOI : 10.1109/CVPR.2009.5206556

M. B. Wakin, D. Baron, and &. G. Baraniuk, Compression of Higher Dimensional Functions Containing Smooth Discontinuities, Annual Conference on Information Science and Systems, p.64, 2004.

D. Chetverikov and &. S. Fazekas, On motion periodicity of dynamic textures, Procedings of the British Machine Vision Conference 2006, pp.167-176, 2006.
DOI : 10.5244/C.20.18

P. L. Combettes and &. V. Wajs, Signal Recovery by Proximal Forward-Backward Splitting, Multiscale Modeling & Simulation, vol.4, issue.4, pp.1168-1200, 2005.
DOI : 10.1137/050626090

URL : https://hal.archives-ouvertes.fr/hal-00017649

]. P. Combettes and &. J. Pesquet, A Douglas???Rachford Splitting Approach to Nonsmooth Convex Variational Signal Recovery, IEEE Journal of Selected Topics in Signal Processing, vol.1, issue.4, pp.564-574, 2007.
DOI : 10.1109/JSTSP.2007.910264

URL : https://hal.archives-ouvertes.fr/hal-00621820

]. L. Cooper, J. Liu, and &. K. Huang, Spatial Segmentation of Temporal Texture Using Mixture Linear Models, International Conference of Computer Vision (ICCV 06), pp.142-150, 2006.
DOI : 10.1007/978-3-540-70932-9_11

K. G. Derpanis, &. R. Wildes, and M. Vetterli, Dynamic Texture Recognition based on Distributions of Spacetime Oriented Structure The Contourlet Transform : an Efficient Directional Multiresolution Image Representation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR 10), pp.48-2091, 2005.

D. L. Donoho, Orthonormal Ridgelets and Linear Singularities, SIAM Journal on Mathematical Analysis, vol.31, issue.5, pp.1062-1099, 1998.
DOI : 10.1137/S0036141098344403

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.379.8363

]. D. Donoho-99a and . Donoho, Wedgelets: nearly minimax estimation of edges, The Annals of Statistics, vol.27, issue.3, pp.859-897, 1999.
DOI : 10.1214/aos/1018031261

]. D. Donoho-99b, &. M. Donoho, and . Duncan, Digital Curvelet Transform : Strategy, Implementation and Experiments, Wavelet Applications VII, pp.12-29, 1999.

]. D. Donoho, X. Huo, I. Jermyn, P. Jones, G. Lerman et al., Beamlets and Multiscale Image Analysis, Multiscale and Multiresolution Methods, pp.149-196, 2001.
DOI : 10.1007/978-3-642-56205-1_3

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.6858

]. G. Doretto-03a, A. Doretto, Y. N. Chiuso, &. S. Wu, and . Soatto, Dynamic Textures, International Journal of Computer Vision, vol.51, issue.2, pp.91-109, 2003.
DOI : 10.1023/A:1021669406132

]. G. Doretto-03b, D. Doretto, P. Cremers, &. S. Favaro, and . Soatto, Dynamic texture segmentation, Proceedings Ninth IEEE International Conference on Computer Vision, pp.1236-1242, 2003.
DOI : 10.1109/ICCV.2003.1238632

. Bibliographie-[-doretto-03c-]-g, &. S. Doretto, and . Soatto, Editable Dynamic Textures, IEEE Conference on Computer Vision and Pattern Recognition (CVPR 03), pp.137-142, 2003.

]. G. Doretto-03d, &. S. Doretto, and . Soatto, Towards Plenoptic Dynamic Textures, International Workshop on Texture Analysis and Synthesis, pp.25-30, 2003.

]. G. Doretto-04, E. Doretto, &. S. Jones, and . Soatto, Spatially Homogeneous Dynamic Textures, IEEE European Conference on Computer Vision (ECCV 04), pp.591-602, 2004.
DOI : 10.1007/978-3-540-24671-8_47

]. G. Doretto-05b and . Doretto, Modeling Dynamic Scenes with Active Appearance, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.66-73, 2005.
DOI : 10.1109/CVPR.2005.226

G. Doretto and &. S. Soatto, Dynamic Shape and Appearance Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.12, pp.162-8828, 2006.
DOI : 10.1109/TPAMI.2006.243

M. Hamidi, M. Ménard, &. C. Lugiez, and . Ghannam, Weighted and extended total variation for image restoration and decomposition, Pattern Recognition, vol.43, issue.4, pp.1564-1576, 2010.
DOI : 10.1016/j.patcog.2009.10.011

URL : https://hal.archives-ouvertes.fr/hal-00441453

R. Fablet and &. P. Bouthemy, Motion-Based Feature Extraction and Ascendant Hierarchical Classification for Video Indexing and Retrieval, Visual Information and Information Systems, pp.658-658, 1999.

R. Fablet, P. Bouthemy, and &. P. Pérez, Statistical Motion-based Video Indexing and Retrieval, International Conference on Adaptivity, Personalization and Fusion of Heterogeneous Information, pp.602-619, 2000.
URL : https://hal.archives-ouvertes.fr/inria-00072639

]. R. Fablet-01a, &. P. Fablet, and . Bouthemy, Motion recognition using spatio-temporal random walks in sequence of 2D motion-related measurements, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), pp.652-655, 2001.
DOI : 10.1109/ICIP.2001.958203

]. R. Fablet-01b, &. P. Fablet, and . Bouthemy, Non parametric motion recognition using temporal multiscale Gibbs models, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp.501-536, 2001.
DOI : 10.1109/CVPR.2001.990516

R. Fablet, P. Bouthemy, and &. P. Pérez, Nonparametric motion characterization using causal probabilistic models for video indexing and retrieval, IEEE Transactions on Image Processing, vol.11, issue.4, pp.393-407, 2002.
DOI : 10.1109/TIP.2002.999674

R. Fablet and &. P. Bouthemy, Motion recognition using nonparametric image motion models estimated from temporal and multiscale co-occurrence statistics, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.12, pp.1619-1624, 2003.
DOI : 10.1109/TPAMI.2003.1251155

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.8897

]. J. Fadili-09a, &. J. Fadili, and . Starck, Curvelets and Ridgelets, Encyclopedia of Complexity and Systems Science, pp.1718-1738, 2009.

]. J. Fadili-09b, J. L. Fadili, J. Starck, &. Y. Bobin, and . Moudden, Image Decomposition and Separation Using Sparse Representations: An Overview, IEEE Special Issue : Applications of Sparse Representation, p.186, 2009.
DOI : 10.1109/JPROC.2009.2024776

]. J. Fadili-10a and . Fadili, Une exploration desprobì emes inverses par la représentations parcimonieuses et l'optimisation non lisse, p.123, 2010.

]. J. Fadili-10b, J. L. Fadili, M. Starck, &. D. Elad, and . Donoho, MCALab: Reproducible Research in Signal and Image Decomposition and Inpainting, Computing in Science & Engineering, vol.12, issue.1, pp.44-63, 2010.
DOI : 10.1109/MCSE.2010.14

S. Fazekas and &. D. Chetverikov, Normal Versus Complete Flow in Dynamic Texture Recognition : a Comparative Study, International Workshop on Texture Analysis and Synthesis, pp.37-42, 2005.

]. S. Fazekas-07a, &. D. Fazekas, and . Chetverikov, Analysis and performance evaluation of optical flow features for dynamic texture recognition, Signal Processing: Image Communication, vol.22, issue.7-8, pp.680-691, 2007.
DOI : 10.1016/j.image.2007.05.013

]. S. Fazekas-07b, &. D. Fazekas, and . Chetverikov, Dynamic Texture Recognition Using Optical Flow Features and Temporal Periodicity, 2007 International Workshop on Content-Based Multimedia Indexing, pp.25-32, 2007.
DOI : 10.1109/CBMI.2007.385388

S. Fazekas, T. Amiaz, D. Chetverikov, and &. N. Kiryati, Dynamic Texture Detection Based on Motion Analysis, International Journal of Computer Vision, vol.4, issue.9, pp.48-63, 2009.
DOI : 10.1007/s11263-008-0184-y

R. A. Finkel and &. J. Bentley, Quad trees a data structure for retrieval on composite keys, Acta Informatica, vol.4, issue.1, pp.1-9, 1974.
DOI : 10.1007/BF00288933

J. M. Francos, A. Z. Meiri, and &. B. Porat, A unified texture model based on a 2-D Wold-like decomposition, IEEE Transactions on Signal Processing, vol.41, issue.8, pp.2665-2678, 1993.
DOI : 10.1109/78.229897

&. S. Fujita and . Nayar, Recognition of Dynamic Textures using Impulse Responses of State Variables, International Workshop on Texture Analysis and Synthesis, pp.43-183, 2003.

P. Gao and &. C. Xu, Extended Statistical Landscape Features for Dynamic Texture Recognition, 2008 International Conference on Computer Science and Software Engineering, pp.429-459, 1946.
DOI : 10.1109/CSSE.2008.785

M. Bibliographie, &. W. Gervautz, and . Purgathofer, A Simple Method for Color Quantization : Octree Quantization, New Trends in Computer Graphics, pp.287-293, 1990.

]. B. Ghanem-07a, &. N. Ghanem, and . Ahuja, Phase Based Modelling of Dynamic Textures, 2007 IEEE 11th International Conference on Computer Vision, pp.1-8, 2007.
DOI : 10.1109/ICCV.2007.4409094

]. B. Ghanem-07b, &. N. Ghanem, and . Ahuja, Phase PCA for Dynamic Texture Video Compression, 2007 IEEE International Conference on Image Processing, pp.425-428, 2007.
DOI : 10.1109/ICIP.2007.4379337

]. B. Ghanem and &. N. Ahuja, Extracting a fluid dynamic texture and the background from video, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587547

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

]. J. Kennedy and &. R. Eberhart, Particle swarm optimization, Proceedings of ICNN'95, International Conference on Neural Networks, pp.1942-1948, 1995.
DOI : 10.1109/ICNN.1995.488968

. Kobayashi-09a-]-t, T. Kobayashi, T. Higuchi, &. N. Miyajima, and . Otsu, Recognition of Dynamic Texture Patterns Using CHLAC Features, 2009 Symposium on Bio-inspired Learning and Intelligent Systems for Security, pp.58-60, 2009.
DOI : 10.1109/BLISS.2009.31

. Kobayashi-09b-]-t, &. N. Kobayashi, and . Otsu, Three-way auto-correlation approach to motion recognition, Pattern Recognition Letters, vol.30, issue.3, pp.212-221, 2009.
DOI : 10.1016/j.patrec.2008.09.006

]. Z. Koldovsky and &. P. Tichavsky, Methods of Fair Comparison of Performance of Linear ICA Techniques in Presence of Additive Noise, 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, pp.873-876, 0120.
DOI : 10.1109/ICASSP.2006.1661415

C. Li and &. M. Hall, Learning A Stable Structure To Describe Dynamic Texture, Procedings of the British Machine Vision Conference 2008, p.48, 2008.
DOI : 10.5244/C.22.3

]. J. Li-09a, L. Li, &. Y. Chen, and . Cai, Dynamic Texture Segmentation Using 3-D Fourier Transform, International Conference on Image and Graphics (ICIG 09), pp.293-298, 2009.

]. J. Li-09b, L. Li, &. Y. Chen, and . Cai, Dynamic Texture Segmentation Using Fourier Transform, Lions 79] P.L. Lions & B. Mercier. Splitting Algorithms for the Sum of Two Nonlinear Operators, pp.29-36, 1979.

Z. Lu, W. Xie, J. Pei, and &. J. Huang, Dynamic Texture Recognition by Spatio-Temporal Multiresolution Histograms, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05), Volume 1, pp.241-246, 2005.
DOI : 10.1109/ACVMOT.2005.44

&. P. Ma and . Cisar, Event Detection using Local Binary Pattern based Dynamic Textures, IEEE Conference on Computer Vision and Pattern Recognition (CVPR 09), pp.38-44, 2009.

S. Mallat, A wavelet tour of signal processing, p.59, 1998.

]. R. Nelson and &. R. Polana, Qualitative recognition of motion using temporal texture, CVGIP: Image Understanding, vol.56, issue.1, pp.78-89, 1992.
DOI : 10.1016/1049-9660(92)90087-J

&. D. Pietikäinen and . Harwood, A Comparative Study of Texture Measures with Classification based on Featured Distributions, Pattern Recognition, vol.29, pp.51-59, 1996.

&. T. Pietikäinen and . Mäenpää, Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, pp.971-987, 2002.

]. K. Otsuka, T. Horikoshi, S. Suzuki, and &. M. Fujii, Feature extraction of temporal texture based on spatiotemporal motion trajectory, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), pp.1047-1091, 1998.
DOI : 10.1109/ICPR.1998.711871

C. Peh and &. L. Cheong, Exploring Video Content in Extended Spatio- Temporal Textures Synergizing Spatial and Temporal Texture, European workshop on Content-Based Multimedia Indexing, pp.183-1179, 1999.

M. Phillips, &. V. Shah, and . Lobo, Flame recognition in video, Proceedings Fifth IEEE Workshop on Applications of Computer Vision, pp.319-327, 2002.
DOI : 10.1109/WACV.2000.895426

R. Polana and &. R. Nelson, Recognition of motion from temporal texture, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.34-35, 1992.
DOI : 10.1109/CVPR.1992.223216

R. Polana and &. R. Nelson, Temporal Texture and Activity Recognition, Computational Imaging and Vision, vol.9, pp.87-124, 1997.
DOI : 10.1007/978-94-015-8935-2_5

R. Bibliographie, &. D. Péteri, and . Chetverikov, Qualitative Characterization of Dynamic Textures for Video Retrieval, International Conference on Computer Vision and Graphics (ICCVG 04), pp.33-38, 2004.

R. Péteri and &. D. Chetverikov, Dynamic Texture Recognition Using Normal Flow and Texture Regularity, Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 05), pp.223-230, 2005.
DOI : 10.1007/11492542_28

]. R. Péteri-10a and . Péteri, Tracking Dynamic Textures using a Particle Filter Driven by Intrinsic Motion Information. Machine Vision and Applications, pp.1-9, 2010.

]. R. Péteri-10b, S. Péteri, &. M. Fazekas, and . Huiskes, DynTex: A comprehensive database of dynamic textures, Pattern Recognition Letters, vol.31, issue.12, pp.1627-1632, 2010.
DOI : 10.1016/j.patrec.2010.05.009

]. A. Rahman-04a, &. M. Rahman, and . Murshed, Real-time temporal texture characterisation using block based motion co-occurrence statistics, 2004 International Conference on Image Processing, 2004. ICIP '04., pp.1593-1596, 2004.
DOI : 10.1109/ICIP.2004.1421372

]. A. Rahman-04b, M. Rahman, &. L. Murshed, and . Dooley, Feature weighting methods for abstract features applicable to motion based video indexing, International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004., pp.676-714, 2004.
DOI : 10.1109/ITCC.2004.1286544

A. Rahman and &. M. Murshed, A robust optical flow estimation algorithm for temporal textures, International Conference on Information Technology: Coding and Computing (ITCC'05), Volume II, pp.72-76, 2005.
DOI : 10.1109/ITCC.2005.31

]. A. Rahman-07a, &. M. Rahman, and . Murshed, Multiple temporal texture detection using feature space mapping, Proceedings of the 6th ACM international conference on Image and video retrieval, CIVR '07, pp.417-424, 2007.
DOI : 10.1145/1282280.1282342

]. A. Rahman-07b, &. M. Rahman, and . Murshed, A Temporal Texture Characterization Technique Using Block-Based Approximated Motion Measure, IEEE Transactions on Circuits and Systems for Video Technology, pp.1370-1382, 2007.
DOI : 10.1109/TCSVT.2007.903790

A. Ravichandran, R. Chaudhry, and &. R. Vidal, View-invariant dynamic texture recognition using a bag of dynamical systems, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.1651-1657, 2009.
DOI : 10.1109/CVPR.2009.5206847

P. Saisan, G. Doretto, Y. N. Wu, and &. S. Soatto, Dynamic texture recognition, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp.58-63, 2001.
DOI : 10.1109/CVPR.2001.990925

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.5646

A. Schodl, R. Szeliski, D. H. Salesin, and &. I. Essa, Video textures, Proceedings of the 27th annual conference on Computer graphics and interactive techniques , SIGGRAPH '00, pp.489-498, 2000.
DOI : 10.1145/344779.345012

A. W. Smeulders, M. Worring, S. Santini, A. Gupta, and &. R. Jain, Content-based image retrieval at the end of the early years, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.12, pp.1349-1380, 2000.
DOI : 10.1109/34.895972

J. R. Smith, C. Y. Lin, and &. M. Naphade, Video texture indexing using spatio-temporal wavelets, Proceedings. International Conference on Image Processing, pp.437-440, 2002.
DOI : 10.1109/ICIP.2002.1039981

S. Soatto, G. Doretto, and &. Y. Wu, Dynamic textures, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pp.439-446, 2001.
DOI : 10.1109/ICCV.2001.937658

J. L. Starck, E. Candès, and &. D. Donoho, The curvelet transform for image denoising, IEEE Transactions on Image Processing, vol.11, issue.6, pp.670-684, 2000.
DOI : 10.1109/TIP.2002.1014998

J. L. Starck, M. Elad, and &. D. Donoho, Redundant Multiscale Transforms and their Application for Morphological Component Analysis, Advances in Imaging and Electron Physics, pp.121-123, 2004.

J. L. Starck, M. Elad, and &. D. Donoho, Image decomposition via the combination of sparse representations and a variational approach, IEEE Transactions on Image Processing, vol.14, issue.10, pp.1570-1582, 2005.
DOI : 10.1109/TIP.2005.852206

]. M. Szummer-96b, &. R. Szummer, and . Picard, Temporal texture modeling, Proceedings of 3rd IEEE International Conference on Image Processing, pp.823-826, 1996.
DOI : 10.1109/ICIP.1996.560871

L. A. Vese and &. S. Osher, Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing, Journal of Scientific Computing, vol.19, issue.1/3, pp.553-572, 2002.
DOI : 10.1023/A:1025384832106

]. S. Vishwanathan, A. J. Smola, and &. R. Vidal, Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes, International Journal of Computer Vision, vol.4, issue.1, pp.95-119, 2007.
DOI : 10.1007/s11263-006-9352-0

&. S. Wang and . Zhu, Modeling Textured Motion : Particle, wave and Sketch, IEEE International Conference on Computer Vision (ICCV 03), pp.213-220, 2003.

]. R. Willett and &. D. Nowak, Platelets: a multiscale approach for recovering edges and surfaces in photon-limited medical imaging, IEEE Transactions on Medical Imaging, vol.22, issue.3, pp.332-350, 2003.
DOI : 10.1109/TMI.2003.809622

A. Woiselle, J. L. Starck, and &. J. Fadili, Inpainting with 3D Sparse Transforms, p.126, 2009.

A. W. Fitzgibbon, Shift-Invariant Dynamic Texture Recognition, IEEE European Conference on Computer Vision (ECCV 06), pp.549-562, 2006.

]. P. Bibliographie, Y. M. Wu, C. S. Ro, &. Y. Won, and . Choi, Texture Descriptors in MPEG-7, International Conference on Computer Analysis of Images and Patterns (CAIP 01), pp.21-28, 2001.

L. Ying, &. E. Demanet, and . Candès, 3D discrete curvelet transform, Wavelets XI, p.75, 2005.
DOI : 10.1117/12.616205

L. Yuan, F. Wen, C. Liu, and &. Y. Shum, Synthesizing Dynamic Texture with Closed-Loop Linear Dynamic System, IEEE European Conference on Computer Vision (ECCV 04), pp.603-616, 2004.
DOI : 10.1007/978-3-540-24671-8_48

&. M. Pietikäinen, Local Binary Pattern Descriptors for Dynamic Texture Recognition, International Conference on Pattern Recognition (ICPR 06), pp.211-214, 2006.

]. G. Zhao-07a, &. M. Zhao, and . Pietikäinen, Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.6, pp.915-928, 2007.
DOI : 10.1109/TPAMI.2007.1110

G. Zhao and &. M. Pietikäinen, Dynamic Texture Recognition Using Volume Local Binary Patterns, Lecture Notes in Computer Science, vol.4358, issue.47, pp.165-177, 2007.
DOI : 10.1007/978-3-540-70932-9_13

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.91.5398

&. S. Zhong and . Scarlaroff, Temporal Texture Recognition Model Using 3D Features, p.45, 2002.

&. T. Zhou and . Huang, Relevance feedback in image retrieval: A comprehensive review, Multimedia Systems, vol.8, issue.6, pp.536-544, 2003.
DOI : 10.1007/s00530-002-0070-3

]. G. Zhou-09, N. Zhou, &. Y. Dong, and . Wang, Non-Linear Dynamic Texture Analysis and Synthesis Using Constrained Gaussian Process Latent Variable Model, 2009 Pacific-Asia Conference on Circuits, Communications and Systems, pp.27-30, 2009.
DOI : 10.1109/PACCS.2009.30

. Résumé, Nous nous intéresserons dans cette thèsè a l'´ etude et la caractérisation des Textures Dynamiques (TDs), avec comme application visée l

. Enfin, le caractère générique des approches proposées permet d'envisager leurs applications dans un cadre plus large