.. Caractérisation-globale-des-gestes, 89 5.2.1 Caractérisation avec les Flots Optiques : Signature du Geste 89 5.2.2 Caractérisation avec les HOG, p.95

C. Adaptation-des, . Mmc-À-l-'apprentissage-avec-un-seul-exemple, and .. Mmc, 96 5.3.1 Adaptation de la composante des, p.97

.. Résultats-de-reconnaissance-de-gestes, J. Adam, C. Ogier, . Cariou, J. Mullot et al., 101 [1] Joint probabilistic techniques for tracking multi-part objects Utilisation de la transformée de fourrier-mellin pour la reconnaissance de formes multi-orientées et multi-échelles : application à l'analyse automatique des documents techniques Ait-Mohand : Techniques d'adaptation de modèles markoviens. Application à la reconnaissance de documents anciens Aizerman : Review of "syntactic methods in pattern recognition" by king-sun fu Assaleh et A. Tala'a : Video-based signer-independent arabic sign language recognition using hidden markov models, Thèse de doctorat, pp.5717-5750, 1976.

E. Arnaud-arulampalam, S. Maskell, N. Gordon, and T. Clapp, Methodes de filtrage pour du suivi dans des sequences d'images -Application au suivi de points caracteristiques. These, Université Rennes 1, novembre A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking, Trans. Sig. Proc, vol.50, issue.2, pp.12174-188, 2002.

M. Assan, K. Grobel, S. Austin, R. Schwartz, and P. Placeway, Video-based sign language recognition using hidden markov models The forward-backward search algorithm, Proceedings of the International Gesture Workshop on Gesture and Sign Language in Human-Computer Interaction Proceedings of the Acoustics, Speech, and Signal Processing ICASSP-91., 1991 International Conference, ICASSP '91, pp.97-109, 1991.

E. Barbu, Fouille et classification de graphes Application à la reconnaissance de symboles dans les documents techniques, Thèse de doctorat, p.28, 2007.

F. Bashir and F. Porikli, Performance evaluation of object detection and tracking systems, IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), pp.31-49, 2006.

L. E. Baum and T. Petrie, Statistical Inference for Probabilistic Functions of Finite State Markov Chains, The Annals of Mathematical Statistics, vol.37, issue.6, pp.1554-1563, 1966.
DOI : 10.1214/aoms/1177699147

Y. Bengio, Y. Lecun, C. Nohl, and C. Burges, LeRec: A NN/HMM Hybrid for On-Line Handwriting Recognition, Neural Computation, vol.3, issue.6, pp.1289-1303, 1995.
DOI : 10.1109/34.57669

J. Donald, J. Berndt, and . Clifford, Using Dynamic Time Warping to Find Patterns in Time Series, KDD Workshop, pp.359-370, 1994.

M. Suchendra, X. Bhandarkar, and . Luo, Integrated detection and tracking of multiple faces using particle filtering and optical flow-based elastic matching, CVIU, vol.113, issue.6, pp.708-725, 2009.

A. Bhattacharyya, On a measure of divergence between two statistical populations defined by their probability distributions, Bulletin of the Calcutta Mathematical Society, vol.35, pp.99-109, 1943.

Y. Bin and . Peng-jia-xiong, Improvement and invariance analysis of zernike moments using as a region-based shape descriptor, Proceedings of the 15th Brazilian Symposium on Computer Graphics and Image Processing, SIBGRAPI '02, pp.120-149, 2002.

A. C. Bovik, Analysis of multichannel narrow-band filters for image texture segmentation, IEEE Transactions on Signal Processing, vol.39, issue.9, pp.2025-2043, 1991.
DOI : 10.1109/78.134435

G. Bradski and A. Kaehler, Learning OpenCV : Computer Vision with the OpenCV Library, pp.44-100, 2008.

R. Gary and . Bradski, Computer vision face tracking for use in a perceptual user interface, Intel Technology Journal, p.132, 1998.

A. Braffort, ARGo: An Architecture for Sign Language Recognition and Interpretation, Proceedings of Gesture Workshop on Progress in Gestural Interaction, pp.17-30, 1997.
DOI : 10.1007/978-1-4471-0943-3_3

L. Bretzner, I. Laptev, and T. Lindeberg, Hand gesture recognition using multi-scale colour features, hierarchical models and particle filtering, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, pp.423-447, 2002.
DOI : 10.1109/AFGR.2002.1004190
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.1218

R. Brunelli, O. Mich, and C. M. , A Survey on the Automatic Indexing of Video Data,, Journal of Visual Communication and Image Representation, vol.10, issue.2, pp.78-112, 1996.
DOI : 10.1006/jvci.1997.0404

A. Bugeau and P. Pérez, Detection and segmentation of moving objects in highly dynamic scenes, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
DOI : 10.1109/CVPR.2007.383244
URL : https://hal.archives-ouvertes.fr/hal-00551596

V. Caselles and F. Catté, A geometric model for active contours in image processing, Numerische Mathematik, vol.36, issue.4, pp.1-31, 1993.
DOI : 10.1007/BF01385685

S. Cho, H. Yang, . Seong-whan, and . Lee, Sign language spotting based on semi-Markov Conditional Random Field, 2009 Workshop on Applications of Computer Vision (WACV), pp.1-6, 2009.
DOI : 10.1109/WACV.2009.5403109

J. Gark, C. Seong-dae, and . Kim, Multi-stage segmentation of optical flow field, Signal Processing, vol.54, issue.2, pp.109-118, 1996.

C. Chong, P. Raveendran, and R. Mukundan, Translation invariants of Zernike moments, Pattern Recognition, vol.36, issue.8, pp.1765-1773, 2003.
DOI : 10.1016/S0031-3203(02)00353-9

L. Cinque, S. Levialdi, and K. A. Olsen, Pellicanò : Color-based image retrieval using spatial-chromatic histograms, ICMCS, pp.969-973, 1999.
DOI : 10.1016/s0262-8856(01)00060-9

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), pp.142-149, 2000.
DOI : 10.1109/CVPR.2000.854761

A. Corradini, Real-Time Gesture Recognition by Means of Hybrid Recognizers, pp.34-46, 2001.
DOI : 10.1007/3-540-47873-6_4

I. J. Cox, A review of statistical data association techniques for motion correspondence, International Journal of Computer Vision, vol.7, issue.3, pp.53-66, 1993.
DOI : 10.1007/BF01440847

N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.886-893, 2005.
DOI : 10.1109/CVPR.2005.177
URL : https://hal.archives-ouvertes.fr/inria-00548512

P. Dalle, A. Braffort, and C. Collet, Accessibilité et langue des signes : modélisations, méthodes, application (regular (long) paper) Conférence Internationale sur l'accessibilité et les systèmes de suppléance aux personnes en situations de handicaps (ASSISTH), pp.209-217, 2007.

A. P. Dempster, N. M. Laird, and D. B. , Rubin : Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society : Series B, vol.39, pp.1-38, 1977.

S. Feng, R. Manmatha, and A. Mccallum, Exploring the use of conditional random field models and hmms for historical handwritten document recognition, the Proceedings of the 2nd IEEE International Conference on Document Image Analysis for Libraries (DIAL), pp.30-37, 2006.

J. D. Ferguson, Variable duration models for speech. page 143 ?179, pp.63-67, 1980.

S. Rogerio, V. Feris, R. M. Krueger, and J. Cesar, A wavelet subspace method for real-time face tracking. Real-Time Imaging, pp.339-350, 2004.

H. Freeman, On the encoding of arbitrary geometric configurations Institute of Radio Engineers, trans. on Electronic Computers, pp.260-268, 1961.

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

M. M. Galloway, Texture analysis using gray level run lengths, Computer Graphics and Image Processing, vol.4, issue.2, pp.172-179, 1975.
DOI : 10.1016/S0146-664X(75)80008-6

A. Ganapathiraju, J. Hamaker, and J. Picone, Hybrid svm/hmm architectures for speech recognition, INTERSPEECH, pp.504-507, 2000.

F. Gianni, Suivi de parties du corps pour l'interprétation de gestes de communication à partir de séquence monoculaire, Thèse de doctorat, p.24, 2007.

F. Gianni, C. Collet, and F. Lefebvre, Modèles et méthodes de traitement d'images pour l'analyse de la langue des signes, TAL, vol.48, issue.26, pp.175-200, 2007.

S. Gidel, C. Aubiere, T. Blanc, P. Chateau, and . Checchin, Nonparametric data association for particle filter based multi-object tracking : application to multipedestrian tracking. Intelligent Vehicles Symposium, pp.73-78, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00344115

M. Gilloux, B. Lemarié, and M. Leroux, A hybrid radial basis function network/hidden Markov model handwritten word recognition system, Proceedings of 3rd International Conference on Document Analysis and Recognition, pp.394-397, 1995.
DOI : 10.1109/ICDAR.1995.599021

N. J. Gordon, D. J. Salmond, and A. F. Smith, Novel approach to nonlinear/non-Gaussian Bayesian state estimation. Radar and Signal Processing, IEE Proceedings F, vol.140, issue.12, pp.107-113, 2002.

P. Gérard and A. Gagalowicz, Three dimensional model-based tracking using texture learning and matching, Pattern Recognition Letters, vol.21, issue.13-14, pp.13-141095, 2000.
DOI : 10.1016/S0167-8655(00)00067-2

W. E. Grimson, C. Stauffer, R. Romano, and L. Lee, Using adaptive tracking to classify and monitor activities in a site, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231), pp.22-29, 1998.
DOI : 10.1109/CVPR.1998.698583

K. Grobel and M. Assam, Isolated sign language recognition using hidden Markov models, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, pp.162-167, 1997.
DOI : 10.1109/ICSMC.1997.625742

A. Gunawardana, M. Mahajan, A. Acero, and J. C. Platt, Hidden conditional random fields for phone classification, INTERSPEECH, pp.1117-1120, 2005.

I. Guyon, V. Athitsos, P. Jangyodsuk, B. Hamner, and H. Escalante, ChaLearn gesture challenge: Design and first results, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp.1-6, 1998.
DOI : 10.1109/CVPRW.2012.6239178
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.298.8603

R. M. Haralick and K. , Shanmugam et Its'Hak Dinstein : Textural Features for Image Classification. Systems, Man and Cybernetics, IEEE Transactions, issue.36, pp.610-621, 1973.

C. Harris and M. Stephens, A Combined Corner and Edge Detector, Procedings of the Alvey Vision Conference 1988, pp.147-151, 1988.
DOI : 10.5244/C.2.23

D. Hébert, Champs aléatoires conditionnels pour l'extraction de structures dans les images de documents, Thèse de doctorat, pp.63-77, 2013.

A. J. Hii, C. E. Hann, J. G. Chase, and E. W. Eli, Fast normalized cross correlation for motion tracking using basis functions, Computer Methods and Programs in Biomedicine, vol.82, issue.2, pp.144-156, 2006.
DOI : 10.1016/j.cmpb.2006.02.007

M. Hu, Visual pattern recognition by moment invariants Information Theory, IRE Transactions on, vol.8, issue.2, pp.179-187, 1962.

M. Isard and A. Blake, 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

P. Jaccard, Bulletin de la Société Vaudoise des Sciences Naturelles, p.31, 1901.

E. Jackson, An hmm-based approach for gesture recognition using edge features, CVPR 2012 Workshop on Gesture Recognition, pp.68-96, 2012.

R. Jain and H. H. Nagel, On the Analysis of Accumulative Difference Pictures from Image Sequences of Real World Scenes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.1, issue.2, pp.206-214, 1979.
DOI : 10.1109/TPAMI.1979.4766907

A. Jepson and M. J. Black, Mixture models for optical flow computation In Computer Vision and Pattern Recognition, Proceedings CVPR '93. IEEE Computer Society Conference on, pp.760-761, 1993.

T. Finn and . Johansen, A comparison of hybrid hmm architectures using global discriminative training, ICSLP. ISCA, p.78, 1996.

R. Kalman, A New Approach to Linear Filtering and Prediction Problems, Journal of Basic Engineering, vol.82, issue.1, pp.35-45, 1960.
DOI : 10.1115/1.3662552

S. Kamijo and M. Sakauchi, Illumination invariant and occlusion robust vehicle tracking by spatio-temporal mrf model, Proc. 9th World Congress on ITS, p.12, 2002.
DOI : 10.1109/itsc.2000.881083

M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active contour models, International Journal of Computer Vision, vol.5, issue.6035, pp.321-331, 1988.
DOI : 10.1007/BF00133570
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.5318

H. Kim and J. Kim, Region-based shape descriptor invariant to rotation, scale and translation, Signal Processing : Image Communication, pp.87-93, 2000.
DOI : 10.1016/S0923-5965(00)00018-7

G. Kitagawa, Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models, Journal of Computational and Graphical Statistics, vol.5, issue.1, pp.1-25, 1996.

J. Klein, Suivi des objets dans des séquences d'images par fusion de sources : application au suivi de véhicules dans des scènes routières, Thèse de doctorat, pp.29-31, 2008.

S. Knerr and E. Augustin, A neural network-hidden Markov model hybrid for cursive word recognition, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), pp.1518-1520, 1998.
DOI : 10.1109/ICPR.1998.711996

J. Konencny and M. Hagara, One-shot learning gesture recognition using hog/hof features, pp.68-69, 2012.

J. D. Lafferty, A. Mccallum, C. N. Fernando, and . Pereira, Conditional random fields : Probabilistic models for segmenting and labeling sequence data, Proceedings of the Eighteenth International Conference on Machine Learning , ICML '01, pp.282-289, 2001.

I. Laptev, S. J. Belongie, P. Perez, and J. Wills, Periodic motion detection and segmentation via approximate sequence alignment, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.816-823, 2005.
DOI : 10.1109/ICCV.2005.188
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.74.1914

C. Lemaître, Utilisation de masques binaries dans une rétine de recon-naissance des formes, p.29, 2005.

B. Lenseigne, F. Gianni, and P. Dalle, A new gesture representation for sign language analysis. LREC 2004 -Workshop on the Representation and Processing of Sign Language : From SignWriting to Image Processing, pp.85-90, 2004.

V. Levenshtein, Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics Doklady, vol.10, pp.707-99, 1966.

P. Li, T. Zhang, and B. Ma, Unscented Kalman filter for visual curve tracking, Statistical Methods in Video Processing, pp.157-164, 2004.
DOI : 10.1016/j.imavis.2003.07.004

T. Lin, A comparative study of zernike moments, Intelligence, vol.1, pp.4-7, 2003.

J. Luis, L. , and J. Morel, Detection of major changes in satellite images, pp.941-944, 2003.

D. C. Liu and J. Nocedal, On the limited memory BFGS method for large scale optimization, Mathematical Programming, vol.32, issue.2, pp.503-528, 1989.
DOI : 10.1007/BF01589116

S. Jun, R. Liu, and . Chen, Sequential monte carlo methods for dynamic systems, Journal of the American Statistical Association, vol.93, pp.1032-1044, 1998.

W. Lu, K. Okuma, and J. James, Tracking and recognizing actions of multiple hockey players using the boosted particle filter, Image and Vision Computing, vol.27, issue.1-2, pp.189-205, 2009.
DOI : 10.1016/j.imavis.2008.02.008

D. Bruce, T. Lucas, and . Kanade, An iterative image registration technique with an application to stereo vision, Int Joint Conf Artif Intel de IJCAI'81, pp.674-679, 1981.

J. Maccormick and M. Isard, Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking, pp.3-19, 2000.
DOI : 10.1007/3-540-45053-X_1

S. Marukatat, T. Artières, P. Gallinari, and B. Dorizzi, Sentence recognition through hybrid neuro-Markovian modeling, Proceedings of Sixth International Conference on Document Analysis and Recognition, pp.731-78, 2001.
DOI : 10.1109/ICDAR.2001.953886

O. Matan, C. J. Burges, Y. Le-cun, and S. John, Denker : Multi-digit recognition using a space displacement neural network, Advances in Neural Information Processing Systems 4, pp.488-495, 1992.

R. Mehrotra and J. E. Gary, Similar-shape retrieval in shape data management, Computer, vol.28, issue.9, pp.57-62, 1995.
DOI : 10.1109/2.410154

A. Magdi, P. D. Mohamed, and . Gader, Generalized hidden markov models. ii. application to handwritten word recognition, IEEE T. Fuzzy Systems, vol.8, issue.1, pp.82-94, 2000.

L. Morency, A. Quattoni, and T. Darrell, Latentdynamic discriminative models for continuous gesture recognition. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, vol.0, pp.1-8, 2007.

N. Morgan, H. Bourlard, S. Renls, M. Cohen, and H. Franco, Hybrid neural network/hidden markov model systems for continuous speech recognition, IJPRAI, vol.7, issue.4, p.78, 1993.

M. E. Morita, R. Sabourin, F. Bortolozzi, and C. Y. Suen, Segmentation and recognition of handwritten dates: an HMM-MLP hybrid approach, International Journal on Document Analysis and Recognition, vol.6, issue.4, pp.248-262, 2003.
DOI : 10.1007/s10032-003-0114-z

C. , E. Moxey, S. J. Sangwine, and A. Todd, Ell : Hypercomplex correlation techniques for vector images, IEEE Transactions on Signal Processing, vol.51, issue.7, pp.1941-1953, 2003.

S. Mukherjee and S. Mitra, HIDDEN MARKOV MODELS, GRAMMARS, AND BIOLOGY: A TUTORIAL, Journal of Bioinformatics and Computational Biology, vol.03, issue.02, pp.491-526, 2005.
DOI : 10.1142/S0219720005001077

C. Neidle, J. Kegl, D. Maclaughli, B. Bahan, and R. G. Lee, The Syntax of American Sign Language-Functional Categories and Hierarchical Structure, p.34, 2000.

C. Neidle, S. Sclaroff, and V. Athitsos, SignStream: A tool for linguistic and computer vision research on visual-gestural language data, Behavior Research Methods, Instruments, & Computers, vol.8, issue.3, pp.311-320, 2001.
DOI : 10.3758/BF03195384

H. T. Nguyen and A. W. , Fast occluded object tracking by a robust appearance filter, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.8, pp.1099-1104, 2004.
DOI : 10.1109/TPAMI.2004.45

L. T. Niles and H. F. Silverman, Combining hidden Markov model and neural network classifiers, International Conference on Acoustics, Speech, and Signal Processing, pp.417-420, 1990.
DOI : 10.1109/ICASSP.1990.115724

J. M. Odobez and P. Bouthemy, Robust Multiresolution Estimation of Parametric Motion Models, Journal of Visual Communication and Image Representation, vol.6, issue.4, p.29, 1995.
DOI : 10.1006/jvci.1995.1029

S. Ong, S. C. , and R. , Deciphering gestures with layered meanings and signer adaptation. Automatic Face and Gesture Recognition, pp.559-68, 2004.

V. Pashaloudi and K. G. Margaritis, Feature extraction and sign recognition for greek sign language, Proceedings of the 11th IEEE Mediterranean Conference on Control and Automation, p.68, 2003.

G. Pass, R. Zabih, and J. Miller, Comparing images using color coherence vectors, Proceedings of the fourth ACM international conference on Multimedia , MULTIMEDIA '96, pp.65-73, 1996.
DOI : 10.1145/244130.244148
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.9596

B. , P. , and B. Meshram, Content based video retrieval systems, International Journal of UbiComp (IJU), vol.3, issue.2, p.4, 2012.

P. Perez, J. Vermaak, and A. Blake, Data Fusion for Visual Tracking With Particles, Proceedings of the IEEE, pp.495-513, 2004.
DOI : 10.1109/JPROC.2003.823147

K. Michael, N. Pitt, and . Shephard, Filtering via Simulation : Auxiliary Particle Filters, Journal of the American Statistical Association, vol.94, issue.446, pp.590-599, 1999.

J. M. Prewitt, Object enhancement and extraction. Picture Processing and Psichopictorics, p.28, 1970.

A. Quattoni, M. Collins, and T. Darrell, Conditional random fields for object recognition, NIPS, pp.1097-1104, 2004.

R. Lawrence and . Rabiner, A tutorial on hidden markov models and selected applications in speech recognition, Proceedings of the IEEE, pp.257-286, 1989.

R. Lawrence and . Rabiner, Readings in speech recognition. chapitre A tutorial on hidden Markov models and selected applications in speech recognition, pp.267-296

S. Rajko and G. Qian, A Hybrid HMM/DPA Adaptive Gesture Recognition Method, ISVC, pp.227-234, 2005.
DOI : 10.1007/11595755_28

D. Reid, An algorithm for tracking multiple targets, IEEE Transactions on Automatic Control, vol.24, issue.6, pp.843-854, 1979.
DOI : 10.1109/TAC.1979.1102177

Y. Ren, C. Chua, and Y. Ho, Statistical background modeling for non-stationary camera, Pattern Recognition Letters, vol.24, issue.1-3, pp.183-196, 2003.
DOI : 10.1016/S0167-8655(02)00210-6

J. Revaud, G. Lavoué, and A. Baskurt, Une nouvelle mesure de distance entre descripteurs de moments de Zernike pour une similarité optimale et un angle de rotation entre les images, CORESA, mars, pp.28-44, 2009.

G. Rigoll, Maximum mutual information neural networks for hybrid connectionist-HMM speech recognition systems, IEEE Transactions on Speech and Audio Processing, vol.2, issue.1, pp.175-184, 1994.
DOI : 10.1109/89.260360

B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter : Particle Filters for Tracking Applications, p.15, 2004.

C. Royère, D. Gruyer, and V. Cherfaoui, Data association with believe theory, Proceedings of the Third International Conference on Information Fusion, p.12, 2000.
DOI : 10.1109/IFIC.2000.862698

Y. Rubner, C. Tomasi, and L. J. Guibas, The earth mover's distance as a metriccfor image retrieval, International Journal of Computer Vision, vol.40, p.44, 2000.

S. Sarawagi and W. W. Cohen, Semi-markov conditional random fields for information extraction, pp.1185-1192, 2004.

M. Sarfraz, Y. A. Syed, and M. , Zeeshan : A system for sign language recognition using fuzzy object similarity tracking, IV '05 : Proceedings of the Ninth International Conference on Information Visualisation, pp.233-238, 2005.

M. Kenneth and . Sayre, Machine recognition of handwritten words : A project report, Pattern Recognition, vol.5, issue.3, pp.213-228, 1973.

I. K. Sethi and R. Jain, Finding Trajectories of Feature Points in a Monocular Image Sequence, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.9, issue.1, pp.56-73, 1987.
DOI : 10.1109/TPAMI.1987.4767872

C. Shan, T. Tan, and Y. Wei, Real-time hand tracking using a mean shift embedded particle filter, Pattern Recognition, vol.40, issue.7, pp.1958-1970, 2007.
DOI : 10.1016/j.patcog.2006.12.012

J. Shi and J. Malik, Motion segmentation and tracking using normalized cuts, ICCV, pp.1154-1160, 1998.

L. Sigal, S. Sclaroff, and V. Athitsos, Skin color-based video segmentation under time-varying illumination, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.7, pp.862-877, 2003.
DOI : 10.1109/TPAMI.2004.35
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.7689

Y. Soullard, Artières : Hybrid hmm and hcrf model for sequence classification, Bruges (Belgium), p.78, 2011.

B. Stenger, A. Thayananthan, P. H. Torr, and R. Cipolla, Model-based hand tracking using a hierarchical Bayesian filter, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.9, pp.1372-1384, 2006.
DOI : 10.1109/TPAMI.2006.189

W. Stokoe, Sign Language Structure, Annual Review of Anthropology, vol.9, issue.1, pp.3-37, 1960.
DOI : 10.1146/annurev.an.09.100180.002053

J. Michael, D. H. Swain, and . Ballard, Color indexing, Int. J. Comput. Vision, vol.7, issue.133, pp.11-32, 1991.

N. Tanibata, N. Shimada, and Y. Shirai, Extraction of hand features for recognition of sign language words, International Conference on Vision Interface, pp.391-398, 2002.

J. Tebelskis and A. Waibel, Bojan Petek et Otto Schmidbauer : Continuous speech recognition by linked predictive neural networks, In NIPS, pp.199-205

S. Thomas, C. Chatelain, L. Heutte, and T. Paquet, Combinaison architecture profonde/hmm pour l'extraction de sequences dans des documents manuscrits, CIFED, p.78, 2012.

P. Torma and C. Szepesvári, Enhancing Particle Filters Using Local Likelihood Sampling, Lecture Notes in Computer Science, vol.1, issue.3021, pp.16-27, 2004.
DOI : 10.1007/978-3-540-24670-1_2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.98.9354

E. Trentin, A survey of hybrid ANN/HMM models for automatic speech recognition, Neurocomputing, vol.37, issue.1-4, pp.1-491, 2001.
DOI : 10.1016/S0925-2312(00)00308-8

M. Unser, Texture classification and segmentation using wavelet frames, IEEE Transactions on Image Processing, vol.4, issue.11, pp.1549-1560, 1995.
DOI : 10.1109/83.469936

T. L. Van-kasteren, G. Englebienne, and B. J. Kröse, Activity recognition using semi-markov models on real world smart home datasets, J. Ambient Intell. Smart Environ, vol.2, issue.68, pp.311-325, 2010.

J. H. Velduis and G. W. Brodland, A deformable block-matching algorithm for tracking epithelial cells, Image and Vision Computing, vol.17, issue.12, pp.905-911, 1999.
DOI : 10.1016/S0262-8856(98)00164-4

R. Vidal and Y. Ma, A Unified Algebraic Approach to 2-D and 3-D Motion Segmentation, pp.1-15, 2004.
DOI : 10.1007/978-3-540-24670-1_1

P. Viola, J. Michael, and . Jones, Robust Real-Time Face Detection, International Journal of Computer Vision, vol.57, issue.2, pp.137-154, 2004.
DOI : 10.1023/B:VISI.0000013087.49260.fb
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.9805

A. Viterbi, Error bounds for convolutional codes and an asymptotically optimum decoding algorithm, IEEE Transactions on Information Theory, vol.13, issue.2, pp.260-269, 1967.
DOI : 10.1109/TIT.1967.1054010

C. Vogler and D. Metaxas, A Framework for Recognizing the Simultaneous Aspects of American Sign Language, Computer Vision and Image Understanding, vol.81, issue.3, pp.358-384, 2001.
DOI : 10.1006/cviu.2000.0895

J. Ulrich-von-agris, U. Zieren, B. Canzler, K. Bauer, and . Kraiss, Recent developments in visual sign language recognition, Universal Access in the Information Society, vol.24, issue.12, pp.323-362, 2008.
DOI : 10.1007/s10209-007-0104-x

M. Hanna and . Wallach, Conditional random fields : An introduction. Rapport technique , University of Pennsylvania CIS, pp.5-63, 2004.

A. Eric, R. Wan, and . Van-der-merwe, The unscented kalman filter for nonlinear estimation, pp.153-158, 2000.

A. Sy-bor-wang, L. Quattoni, D. Morency, and . Demirdjian, Hidden Conditional Random Fields for Gesture Recognition, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), pp.1521-1527, 2006.
DOI : 10.1109/CVPR.2006.132

D. Weiss, Hmm based one shot gesture recognition, CVPR 2012 Workshop on Gesture Recognition, pp.68-96, 2012.

R. Lloyd and . Welch, Hidden markov models and the baum-welch algorithm, IEEE Information Theory Society Newsletter, vol.53, issue.4, p.65, 2003.

L. Wixson and M. Hansen, Detecting salient motion by accumulating directionallyconsistent flow, Proceedings of the International Conference on Computer Vision, pp.797-826, 1999.

D. Wu, F. Zhu, and L. Shao, One shot learning gesture recognition from RGBD images, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp.7-12, 2012.
DOI : 10.1109/CVPRW.2012.6239179

H. Yang, . Seong-whan, and . Lee, Robust Sign Language Recognition with Hierarchical Conditional Random Fields, 2010 20th International Conference on Pattern Recognition, pp.2202-2205, 2010.
DOI : 10.1109/ICPR.2010.539

H. Yang, S. Sclaroff, . Seong-whan, and . Lee, Sign Language Spotting with a Threshold Model Based on Conditional Random Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.7, pp.1264-1277, 2009.
DOI : 10.1109/TPAMI.2008.172

Y. Shuying, G. Weimin, Z. Cheng, and H. Pilian, Detecting and tracking moving targets on omnidirectional vision, pp.13-018, 2009.

Y. Yang, I. Saleemi, and M. Shah, Discovering Motion Primitives for Unsupervised Grouping and One-Shot Learning of Human Actions, Gestures, and Expressions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.7, pp.1635-1648, 2013.
DOI : 10.1109/TPAMI.2012.253

A. Yao, D. Uebersax, J. Gall, and L. Van-gool, Tracking People in Broadcast Sports, DAGM-PR, pp.151-161, 2010.
DOI : 10.1007/978-3-642-15986-2_16

T. Yoo and I. Oh, A fast algorithm for tracking human faces based on chromatic histograms, Pattern Recognition Letters, vol.20, issue.10, pp.967-978, 1999.
DOI : 10.1016/S0167-8655(99)00053-7

S. Quan-yuan, V. Sclaroff, and . Athitsos, Automatic 2D Hand Tracking in Video Sequences, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05), Volume 1, pp.250-256, 2005.
DOI : 10.1109/ACVMOT.2005.27

D. Benjamin, . Zarit, J. Boaz, . Super, K. H. Francis et al., Comparison of five color models in skin pixel classification, ICCV'99 Int'l Workshop on, pp.58-63, 1999.

G. Zavaliagkos, S. Austin, J. Makhoul, and R. M. Schwartz, A hybrid continuous speech recognition system using segmental neural nets with hidden markov models, IJPRAI, vol.7, issue.4, pp.949-963, 1993.

D. Zhang and G. Lu, Review of shape representation and description techniques, Pattern Recognition, vol.37, issue.1, pp.1-19, 2004.
DOI : 10.1016/j.patcog.2003.07.008

W. Steven and . Zucker, Toward a model of texture, Computer Graphics and Image Processing, vol.5, issue.2, pp.190-202, 1976.

A. Filtre-de-kalman, 1.1 Théorie Les filtres de Kalman [66] définissent les fonctions de transition f et d'observation g d'une manière déterministe linéaire selon les équations suivantes