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

M. Orkisz and P. Clarysse, Estimation du flot optique en présence de discontinuités : une revue, pp.489-513, 1996.

T. B. Moeslund, A. Hilton, and V. Kruger, A survey of advances in vision-based human motion capture and analysis, Computer Vision and Image Understanding, vol.104, issue.2-3, pp.90-126, 2006.
DOI : 10.1016/j.cviu.2006.08.002

R. Horaud, Vision par ordinateur : outils fondamentaux, Hermes, 1995.
URL : https://hal.archives-ouvertes.fr/inria-00590049

J. L. Coatrieux, Shape and function from motion in medical imaging: part 2, IEEE Engineering in Medicine and Biology Magazine, vol.25, issue.1, pp.29-31, 2005.
DOI : 10.1109/MEMB.2006.1578645
URL : https://hal.archives-ouvertes.fr/inserm-00130043

J. L. Coatrieux, Shape and function from motion in medical imaging: part 2, IEEE Engineering in Medicine and Biology Magazine, vol.25, issue.1, pp.6-21, 2006.
DOI : 10.1109/MEMB.2006.1578645
URL : https://hal.archives-ouvertes.fr/inserm-00130043

J. L. Coatrieux, A look at ... - shape and function from motion in biomedical imaging: part 3, IEEE Engineering in Medicine and Biology Magazine, vol.26, issue.3, pp.81-83, 2007.
DOI : 10.1109/MEMB.2007.364938
URL : https://hal.archives-ouvertes.fr/inserm-00337827

M. Kass, A. Witskin, and D. Terzopoulos, Snakes : Active contours models. in Visual Computer, pp.321-331, 1988.
DOI : 10.1007/bf00133570
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.5318

J. A. Sethian, Level set methods and fast marching methods, Modèles déformables statistiques pour l'analyse de structures 2D dans des séquences d'images. Technique et Science Informatiques, pp.325-354, 1997.

N. Cladel, Optimisation multicritères de contours actifs par algorithmes génétiques : Application à la segmentation de la bouche, Thèse, Université de Rennes 1, p.13, 2005.

T. Corpetti, Estimation et analyse de champs denses de vitesses d'écoulements fluide, Thèse, Université de Rennes 1, 2002. 14. Horn, B. and B. Schunck, Determining Optical Flow, Artificial Intelligence, vol.17, pp.185-203, 1981.

B. D. Lucas and T. Kanade, An iterative Image Registation Technique with an Application to Stereo Vision, Proceedings DARA Image Understanding Workshop, pp.121-130, 1981.

A. Mitiche and H. Sekkati, Optical Flow 3D Segmentation and Interpretation: A Variational Method with Active Curve Evolution and Level Sets, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.11, pp.28-1818, 2006.
DOI : 10.1109/TPAMI.2006.232

C. Vazquez, A. Mitiche, and R. Laganiere, Joint multiregion segmentation and parametric estimation of image motion by basis function representation and level set evolution, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.5, pp.782-793, 2006.
DOI : 10.1109/TPAMI.2006.97

M. Tagliasacchi, A genetic algorithm for optical flow estimation, Image and Vision Computing, vol.25, issue.2, pp.141-147, 2007.
DOI : 10.1016/j.imavis.2006.01.021

E. Arnaud, E. Mémin, and B. Cernuschi-frias, Conditional filters for image sequence-based tracking - application to point tracking, IEEE Transactions on Image Processing, vol.14, issue.1, pp.14-63, 2005.
DOI : 10.1109/TIP.2004.838707
URL : https://hal.archives-ouvertes.fr/inria-00306720

H. Ji and C. Fermuller, A 3D shape constraint on video, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.6, pp.1018-1023, 2006.
DOI : 10.1109/TPAMI.2006.109

N. Papenberg, Highly Accurate Optic Flow Computation with Theoretically Justified Warping, International Journal of Computer Vision, vol.14, issue.3, pp.141-158, 2005.
DOI : 10.1007/s11263-005-3960-y

A. Bruhn, Variational optical flow computation in real time, IEEE Transactions on Image Processing, vol.14, issue.5, pp.608-615, 2005.
DOI : 10.1109/TIP.2005.846018

Y. H. Kim and A. C. Kak, Error analysis of robust optical flow estimation by least median of squares methods for the varying illumination model, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.9, pp.1418-1435, 2006.
DOI : 10.1109/TPAMI.2006.185

Y. Altunbasak, R. M. Mersereau, and A. Patti, A fast parametric motion estimation algorithm with illumination and lens distortion correction, IEEE Transactions on Image Processing, vol.12, issue.4, pp.395-408, 2003.
DOI : 10.1109/TIP.2003.809012

J. M. Odobez and P. Bouthemy, Robust Multiresolution Estimation of Parametric Motion Models, Journal of Visual Communication and Image Representation, vol.6, issue.4, pp.348-365, 1995.
DOI : 10.1006/jvci.1995.1029

E. P. Ong and M. Spann, Robust Optical Flow Computation Based on Least-Medianof-Squares Regression, Int. J. Comput. Vision, issue.1, pp.31-51, 1999.

E. Mémin and P. Pérez, Dense estimation and object-based segmentation of the optical flow with robust techniques, IEEE Transactions on Image Processing, vol.7, issue.5, pp.703-719, 1998.
DOI : 10.1109/83.668027

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

M. J. Black and P. Anandan, A framework for the robust estimation of optical flow, 1993 (4th) International Conference on Computer Vision, pp.231-236, 1993.
DOI : 10.1109/ICCV.1993.378214

O. F. Olsen and M. Nielsen, The Generic Structure of the Optic Flow Field, Journal of Mathematical Imaging and Vision, vol.21, issue.4, pp.37-53, 2006.
DOI : 10.1007/s10851-005-3614-2

Y. H. Kim, A. Martinez, and A. C. Kak, Robust motion estimation under varying illumination, Image and Vision Computing, vol.23, issue.4, pp.365-375, 2005.
DOI : 10.1016/j.imavis.2004.05.010
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.93.7753

J. Weickert and C. Schnorr, A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion, Int. J. Comput. Vision, issue.3, pp.45-245, 2001.

L. Alvarez, J. Weickert, and J. Sanchez, Reliable Estimation of Dense Optical Flow Fields with Large Displacements, International Journal of Computer Vision, vol.39, issue.1, pp.41-56, 2000.
DOI : 10.1023/A:1008170101536

G. Aubert, R. Deriche, and P. Kornprobst, Computing Optical Flow via Variational Techniques, SIAM Journal on Applied Mathematics, vol.60, issue.1, pp.156-182, 1999.
DOI : 10.1137/S0036139998340170

H. H. Nagel and W. Enkelmann, An Investigation of Smoothness Constraints for the Estimation of Displacement Vector Fields from Image Sequences, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.565-593, 1986.
DOI : 10.1109/TPAMI.1986.4767833

F. Heitz and P. Bouthemy, Multimodal estimation of discontinuous optical flow using Markov random fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.12, pp.1217-1232, 1993.
DOI : 10.1109/34.250841
URL : https://hal.archives-ouvertes.fr/inria-00075193

A. Bab-hadiashar and D. Suter, Robust Optic Flow Computation, International Journal of Computer Vision, vol.29, issue.1, pp.59-77, 1998.
DOI : 10.1023/A:1008090730467
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.24.9286

P. Nesi, A. D. Bimbo, and D. Ben-tzvi, A Robust Algorithm for Optical Flow Estimation, Computer Vision and Image Understanding, vol.62, issue.1, pp.59-68, 1995.
DOI : 10.1006/cviu.1995.1041

R. E. Kalman and R. S. Bucy, New Results in Linear Filtering and Prediction Theory, Journal of Basic Engineering, vol.83, issue.1, pp.95-107, 1961.
DOI : 10.1115/1.3658902

A. S. Gelb and J. Uhlmann, Applied Optimal Estimation A General Method for Approximating Nonlinear Transformations of Probability Distributions, 1974.

J. Czyz, B. Ristic, and B. Macq, A particle filter for joint detection and tracking of color objects, Image and Vision Computing, vol.25, issue.8, pp.25-1271, 2007.
DOI : 10.1016/j.imavis.2006.07.027

N. J. Gordon, D. J. Salmond, and A. F. Smith, Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings F Radar and Signal Processing, pp.107-113, 1993.
DOI : 10.1049/ip-f-2.1993.0015

A. Doucet, On Sequential Monte Carlo Sampling Methods for Bayesian Filtering, Statistics and Computing, vol.10, issue.3, pp.197-208, 1998.
DOI : 10.1023/A:1008935410038

M. K. Pitt and N. Shephard, Filtering via Simulation: Auxiliary Particle Filters, Journal of the American Statistical Association, vol.24, issue.446, pp.590-599, 1999.
DOI : 10.1016/0005-1098(71)90097-5
URL : http://www.tandfonline.com/doi/pdf/10.1080/07350015.2015.1052457

V. Lippiello, B. Siciliano, and L. Villani, Adaptive extended Kalman filtering for visual motion estimation of 3D objects, Control Engineering Practice, vol.15, issue.1, pp.123-134, 2007.
DOI : 10.1016/j.conengprac.2006.05.006

A. Dell-'acqua, A. Sarti, and S. Tubaro, 3D Motion from structures of points, lines and planes, Image and Vision Computing, pp.529-549, 2008.

G. Hua and Y. Wu, A decentralized probabilistic approach to articulated body tracking, Computer Vision and Image Understanding, pp.272-283, 2008.
DOI : 10.1016/j.cviu.2006.11.020

D. W. Hansen and R. I. Hammoud, An improved likelihood model for eye tracking. Computer Vision and Image Understanding, pp.220-230, 2007.

J. Dowdall, I. T. Pavlidis, and P. Tsiamyrtzis, Coalitional tracking, Computer Vision and Image Understanding, vol.106, issue.2-3, pp.205-219, 2007.
DOI : 10.1016/j.cviu.2006.08.011

W. C. Lin and Y. Liu, A latice-based MRF model for dynamic near-regular texture tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, issue.5, pp.29-777, 2007.

M. Isard and A. Blake, Condensation : Conditional Density Propagation forVisual Tracking, International Journal of Computer Vision, vol.29, issue.1, pp.5-28, 1998.
DOI : 10.1023/A:1008078328650

M. Isard and A. Blake, Icondensation: Unifying low-level and high-level tracking in a stochastic framework, Lecture Notes in Computer Science, pp.893-908, 1406.
DOI : 10.1007/BFb0055711

Y. Rui and Y. Chen, Better proposal distributions: object tracking using unscented particle filter, Proceedings of the 2001 IEEE Computer Society Conference on computer Vision and Pattern Recognition, pp.786-793, 2001.

S. J. Mckenna and H. Nait-charif, Tracking human motion using auxiliary particle filters and iterated likelihood weighting, Image and Vision Computing, vol.25, issue.6, pp.25-852, 2007.
DOI : 10.1016/j.imavis.2006.06.003

Z. Khan, T. Balch, F. Dellaert, M. , E. Koller-meier et al., MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Traking with Merged and Multiple Measurements Smart particle filtering for highdimensional tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence Computer Vision and Image Understanding, vol.106, issue.121, pp.28-1960, 2006.

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

W. Hu, Principal Axis-Bases Correspondence between Multiple Cameras for People Tracking, IEEE Transations on Pattern Analysis and Machine Intelligence, vol.28, issue.4, pp.663-671, 2006.

D. Cremers, Dynamical Statisctical Shape Priors for Level Set-Sased Tracking, IEEE Transations on Pattern Analysis and Machine Intelligence, issue.8, pp.28-1262, 2006.

R. Munoz-salinas, E. Aguirre, and M. Garcia-silvente, People detection and tracking using stereo vision and color, Image and Vision Computing, vol.25, issue.6, pp.25-995, 2007.
DOI : 10.1016/j.imavis.2006.07.012

M. Isard and A. Blake, Contour tracking by stochastiques propagation of conditional density, Proceedings European Conf. Computer Vision, pp.343-346, 1996.

C. Vogler, Outlier rejection in high-dimensional deformable models, Image and Vision Computing, vol.25, issue.3, pp.274-284, 2007.
DOI : 10.1016/j.imavis.2005.10.010

G. Zhu, Q. Zeng, and C. Wang, Efficient edge-based object tracking, Pattern Recognition, vol.39, issue.11, pp.39-2223, 2006.
DOI : 10.1016/j.patcog.2006.04.020

J. Xiao, J. Chai, T. Kanade-furukawa, and Y. , A Closed-Form Solution to Non-Rigid Shape and Motion Recovery Robust structure and motion from outlines of smooth curved surfaces A Feature-based Approach for Dense Segmentation and Estimation of Large Disparity Motion, International Journal of Computer Vision IEEE Transactions on Pattern Analysis and Machine Intelligence International Journal of Computer Vision, vol.67, issue.2822, pp.233-246, 2006.

P. H. Torr and A. Zisserman, MLESAC: A New Robust Estimator with Application to Estimating Image Geometry, Computer Vision and Image Understanding, vol.78, issue.1, pp.138-156, 2000.
DOI : 10.1006/cviu.1999.0832

C. Kambhamettu, 3D nonrigid motion analysis under small deformations, Image and Vision Computing, vol.21, issue.3, pp.229-245, 2003.
DOI : 10.1016/S0262-8856(02)00041-0
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.94.5382

A. Simon, Cardiac motion estimation in multislice computed tomography imaging using a 4D multiscale surface-volume matching process, Computers in Cardiology, 2005, pp.32-219, 2005.
DOI : 10.1109/CIC.2005.1588076
URL : https://hal.archives-ouvertes.fr/inserm-00159119

M. Li, C. Kambhamettu, and M. Stone, Nonrigid motion recovery for 3D surfaces, Image and Vision Computing, vol.25, issue.3, pp.250-261, 2007.
DOI : 10.1016/j.imavis.2006.01.008

P. H. Torr and C. Davidson, IMPSAC: synthesis of importance sampling and random sample consensus, IEEE Transactions on Pattern Analysis and Machine Intelligence, issue.3, pp.25-354, 2003.

H. Chui, A. Rangarajan-dellaert, and F. , A new point matching algorithm for non-rigid registration Computer Vision and Image Understanding, EM, MCMC, and Chain Flipping for Structure from Motion with Unknown Correspondence. Machine Learning, pp.114-141, 2003.

P. J. Besl and N. D. Mckay, A method for registration of 3-D shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.2, pp.239-256, 1977.
DOI : 10.1109/34.121791

P. Meer, C. V. Stewart, and D. E. Tyler, Robust Computer Vision: An Interdisciplinary Challenge, Computer Vision and Image Understanding, vol.78, issue.1, pp.1-7, 2000.
DOI : 10.1006/cviu.1999.0833

M. A. Fischler and R. C. Bolles, Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography, Comm. of the ACM, pp.24-381, 1981.

J. Duchon, Splines minimizing rotation-invariant semi-norms in Sobolev spaces, pp.85-100, 1975.
DOI : 10.1007/BFb0086566

C. Tomasi and T. Kanade, Shape and motion from image streams under orthography: a factorization method, International Journal of Computer Vision, vol.4, issue.1, pp.137-154, 1992.
DOI : 10.1007/BF00129684

A. I. Comport, E. Marchand, and F. Chaumette, Kinematic sets for real-time robust articulated object tracking, Image and Vision Computing, vol.25, issue.3, pp.25-374, 2007.
DOI : 10.1016/j.imavis.2005.10.005
URL : https://hal.archives-ouvertes.fr/inria-00350642

T. Drummond, R. Cipolla-gross, R. , I. Matthews, and S. Baker, Real-time visual tracking of complex structures, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.7, pp.932-946, 2002.
DOI : 10.1109/TPAMI.2002.1017620

I. Matthews, J. Xiao, and S. Baker, 2D vs. 3D Deformable Face Models: Representational Power, Construction, and Real-Time Fitting, International Journal of Computer Vision, vol.67, issue.2, pp.75-93, 2007.
DOI : 10.1007/s11263-007-0043-2

A. I. Comport, Real-time markerless tracking for augmented reality: the virtual visual servoing framework, IEEE Transactions on Visualization and Computer Graphics, vol.12, issue.4, pp.615-628, 2006.
DOI : 10.1109/TVCG.2006.78
URL : https://hal.archives-ouvertes.fr/inria-00161250

B. Espiau, F. Chaumette, and P. Rives, A new approach to visual servoing in robotics, IEEE Transactions on Robotics and Automation, vol.8, issue.3, pp.313-326, 1992.
DOI : 10.1109/70.143350

F. Chaumette, Une nouvelle approche de la relation vision-commande en robotique, Inria Report, p.1172, 1990.
URL : https://hal.archives-ouvertes.fr/inria-00075386

D. G. Lowe, Fitting parameterized three-dimensional models to images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, issue.5, pp.441-450, 1991.
DOI : 10.1109/34.134043
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.7868

G. Hua and Y. Wu, Sequential mean field variational analysis of structured deformable shapes, Computer Vision and Image Understanding, vol.101, issue.2, pp.87-99, 2006.
DOI : 10.1016/j.cviu.2005.07.004

J. Wang and L. Yin, Static topographic modeling for facial expression recognition and analysis, Computer Vision and Image Understanding, vol.108, issue.1-2, pp.19-34, 2007.
DOI : 10.1016/j.cviu.2006.10.011

J. Tu, H. Tao, and T. Huang, Face as mouse through visual face tracking, Computer Vision and Image Understanding, vol.108, issue.1-2, pp.35-40, 2007.
DOI : 10.1016/j.cviu.2006.11.007

T. F. Cootes, G. J. Edwards, and C. J. Taylor, Active appearance models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.6, pp.681-685, 2001.
DOI : 10.1109/34.927467

V. Blanz and T. Vetter, A morphable model for the synthesis of 3D faces, Proceedings of the 26th annual conference on Computer graphics and interactive techniques , SIGGRAPH '99, pp.187-194, 1999.
DOI : 10.1145/311535.311556

I. Matthews and S. Baker, Active Appearance Models Revisited, International Journal of Computer Vision, vol.60, issue.2, pp.135-164, 2004.
DOI : 10.1023/B:VISI.0000029666.37597.d3
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.8544

S. Romdhani and T. Vetter, Efficient, robust and accurate fitting of a 3D morphable model, Proceedings Ninth IEEE International Conference on Computer Vision, pp.59-62, 2003.
DOI : 10.1109/ICCV.2003.1238314

P. L. Bazin and J. M. Vezien, Integration of geometric elements, Euclidean relations, and motion curves for parametric shape and motion estimation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.12, pp.27-1960, 2005.
DOI : 10.1109/TPAMI.2005.245

E. Marchand and F. Chaumette, Virtual Visual Servoing: a framework for real-time augmented reality, Proceedings EUROGRAPHICS'02 Conference, pp.236-245, 2002.
URL : https://hal.archives-ouvertes.fr/inria-00352096

J. H. Rong, J. L. Coatrieux, and R. Collorec, Motion estimation in digital subtraction angiography, Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society, pp.567-568, 1989.
DOI : 10.1109/IEMBS.1989.95876

G. E. Mailloux, Computer Analysis of Heart Motion from Two-Dimensional Echocardiograms, IEEE Transactions on Biomedical Engineering, vol.34, issue.5, pp.356-364, 1987.
DOI : 10.1109/TBME.1987.325967

E. A. Zerhouni, Human heart: tagging with MR imaging--a method for noninvasive assessment of myocardial motion., Radiology, vol.169, issue.1, pp.59-63, 1988.
DOI : 10.1148/radiology.169.1.3420283

S. M. Song and R. M. Leahy, Computation of 3-D velocity fields from 3-D cine CT images of a human heart, IEEE Transactions on Medical Imaging, vol.10, issue.3, pp.295-306, 1991.
DOI : 10.1109/42.97579

J. L. Prince and E. R. Mcveigh, Motion estimation from tagged MR image sequences, IEEE Transactions on Medical Imaging, vol.11, issue.2, pp.238-249, 1992.
DOI : 10.1109/42.141648

M. Garreau, Assessment of Left Ventricular Function in Cardiac MSCT Imaging by a 4D Hierarchical Surface-Volume Matching Process, International Journal of Biomedical Imaging, vol.105, issue.4, pp.1-10, 2006.
DOI : 10.1155/IJBI/2006/37607
URL : https://hal.archives-ouvertes.fr/inserm-00147238

P. Clarysse, D. Friboulet, and I. E. Magnin, Tracking geometrical descriptors on 3-D deformable surfaces: application to the left-ventricular surface of the heart, IEEE Transactions on Medical Imaging, vol.16, issue.4, pp.392-404, 1997.
DOI : 10.1109/42.611349

P. Clarysse, D. Friboulet, and I. E. Magnin, Curvature assessment of the heart walls in 3D dynamic imaging, Proceedings of Computers in Cardiology Conference, pp.607-610, 1993.
DOI : 10.1109/CIC.1993.378329

A. A. Young and L. Axel, Three-dimensional motion and deformation of the heart wall: estimation with spatial modulation of magnetization--a model-based approach., Radiology, vol.185, issue.1, pp.185-241, 1992.
DOI : 10.1148/radiology.185.1.1523316

S. Ruan, A. Bruno, and J. L. Coatrieux, Three-dimensional motion and reconstruction of coronary arteries from biplane cineangiography, Image and Vision Computing, vol.12, issue.10, pp.12-683, 1994.
DOI : 10.1016/0262-8856(94)90043-4

E. Hansis, An iterative method for the reconstruction of the coronary arteries from rotational X-ray angiography Physics of Medical Imaging, Proceedings SPIE Medical Imaging, 2007.

C. Blondel, G. Malandain, R. Vaillant, and N. Ayache, Reconstruction of coronary arteries from a single rotational X-ray projection sequence, IEEE Transactions on Medical Imaging, vol.25, issue.5, pp.25-653, 2006.
DOI : 10.1109/TMI.2006.873224
URL : https://hal.archives-ouvertes.fr/inria-00614993

S. Bonnet, Dynamic X-ray computed tomography, Proceedings of the IEEE, pp.91-1574, 2003.
DOI : 10.1109/JPROC.2003.817868