I. Abramson, Arbitrariness of the pilot estimator in adaptive kernel methods, Journal of Multivariate Analysis, vol.12, issue.4, pp.562-567, 1982.
DOI : 10.1016/0047-259X(82)90063-X

S. Allassonnì-ere, Y. Amit, and A. Trouvé, Toward a coherent statistical framework for dense deformable template estimation, Journal of the Royal Statistical Society, vol.69, pp.3-29, 2007.

T. W. Anderson, An Introduction to Multivariate Statistical Analysis, 1984.

H. Asada and M. Brady, The Curvature Primal Sketch, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.8, issue.1, pp.2-14, 1986.
DOI : 10.1109/TPAMI.1986.4767747

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

J. Baerentzen and H. Aanaes, Signed Distance Computation Using the Angle Weighted Pseudonormal, IEEE Transactions on Visualization and Computer Graphics, vol.11, issue.3, pp.243-253, 2005.
DOI : 10.1109/TVCG.2005.49
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.107.9173

E. Bardinet, L. D. Cohen, and N. Ayache, Tracking medical 3D data with a deformable parametric model, ECCV, pp.317-328, 1996.
URL : https://hal.archives-ouvertes.fr/inria-00615756

A. J. Bell and T. J. Sejnowski, An Information-Maximization Approach to Blind Separation and Blind Deconvolution, Neural Computation, vol.20, issue.1, pp.1129-1159, 1995.
DOI : 10.1109/78.301850

S. Belongie, J. Malik, and J. Puzicha, Matching shapes, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pp.456-461, 2001.
DOI : 10.1109/ICCV.2001.937552

S. Berretti, A. Bimbo, and P. Pala, Retrieval by shape using multidimensional indexing structures, Proceedings 10th International Conference on Image Analysis and Processing, p.945, 1999.
DOI : 10.1109/ICIAP.1999.797717

J. Besag, On the statistical analysis of dirty images, Journal of Royal Statistics Society, vol.48, pp.259-302, 1986.

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, 1992.
DOI : 10.1109/34.121791

H. Blum, A Transformation for Extracting New Descriptors of Shape, editor, Models for the Perception of Speech and Visual Form, pp.362-380, 1967.

F. Bookstein, Principal warps: thin-plate splines and the decomposition of deformations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.6, pp.567-585, 1989.
DOI : 10.1109/34.24792

G. Borgefors, Distance Transformations in Digital Images Computer Vision, Graphics and Image Processing, pp.344-371, 1986.

J. Bosch, S. Mitchell, B. Lelieveldt, F. Nijland, O. Kamp et al., Automatic segmentation of echocardiographic sequences by active appearance motion models, IEEE Transactions on Medical Imaging, vol.21, issue.11, pp.1374-1383, 2002.
DOI : 10.1109/TMI.2002.806427

Y. Boykov and V. Kolmogorov, An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.9, pp.1124-1137, 2004.
DOI : 10.1109/TPAMI.2004.60

L. Breiman, W. Meisel, and E. Purcell, Variable Kernel Estimates of Multivariate Densities, Technometrics, vol.21, issue.6, pp.135-144, 1977.
DOI : 10.1214/aoms/1177704472

O. Camara, G. Delso, and I. Bloch, Free Form Deformations Guided by Gradient Vector Flow: A Surface Registration Method in Thoracic and Abdominal PET-CT Applications, WBIR, pp.224-233, 2003.
DOI : 10.1007/978-3-540-39701-4_24

A. Can, C. Stewart, B. Roysam, and H. Tanenbaum, A feature-based, robust, hierarchical algorithm for registering pairs of images of the curved human retina, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.3, pp.347-364, 2002.
DOI : 10.1109/34.990136

J. Canny, A computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.8, pp.769-798, 1986.

J. Cardoso, High-Order Contrasts for Independent Component Analysis, Neural Computation, vol.140, issue.1, pp.157-192, 1999.
DOI : 10.1109/78.599941
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.6720

V. Caselles, R. Kimmel, and G. Sapiro, Geodesic active contours, Proceedings of IEEE International Conference on Computer Vision, pp.694-699, 1995.
DOI : 10.1109/ICCV.1995.466871
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.2196

T. Chan and L. Vese, An Active Contour Model without Edges, International Conference on Scale-Space Theories in Computer Vision, pp.141-151, 1999.
DOI : 10.1007/3-540-48236-9_13
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.6740

T. Chan and L. Vese, Active contours without edges, IEEE Transactions on Image Processing, vol.10, issue.2, pp.266-277, 2001.
DOI : 10.1109/83.902291

G. Charpiat, O. Faugeras, and R. Keriven, Approximations of Shape Metrics and Application to Shape Warping and Empirical Shape Statistics, Foundations of Computational Mathematics, vol.5, issue.1, pp.1-58, 2005.
DOI : 10.1007/s10208-003-0094-x
URL : https://hal.archives-ouvertes.fr/inria-00071766

G. Charpiat, P. Maurel, J. Pons, R. Keriven, and O. Faugeras, Generalized Gradients: Priors on Minimization Flows, International Journal of Computer Vision, vol.80, issue.3, pp.325-344, 2007.
DOI : 10.1007/s11263-006-9966-2
URL : https://hal.archives-ouvertes.fr/hal-01117529

H. Chen and P. Meer, Robust Computer Vision through Kernel Density Estimation, ECCV (1), pp.236-250, 2002.
DOI : 10.1007/3-540-47969-4_16
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.6.8305

Y. Chen and G. Medioni, Object modeling by registration of multiple range images, Proceedings. 1991 IEEE International Conference on Robotics and Automation, pp.145-155, 1992.
DOI : 10.1109/ROBOT.1991.132043

Y. Chen, H. Thiruvenkadam, H. Tagare, F. Huang, and D. Wilson, On the Incorporation of Shape Priors int Geometric Active Contours, IEEE Workshop in Variational and Level Set Methods, pp.145-152, 2001.

Y. Cheng, Mean shift, mode seeking, and clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.17, issue.8, pp.790-799, 1995.
DOI : 10.1109/34.400568

H. Chui and A. Rangarajan, A new point matching algorithm for non-rigid registration, IEEE Conference on Computer Vision and Pattern Recognition, pp.44-51, 2000.
DOI : 10.1016/S1077-3142(03)00009-2

H. Chui and A. Rangarajan, A new point matching algorithm for non-rigid registration, Computer Vision and Image Understanding, vol.89, issue.2-3, pp.114-141, 2003.
DOI : 10.1016/S1077-3142(03)00009-2

I. Cohen and L. D. Cohen, A Hybrid Hyperquadric Model for 2-D and 3-D Data Fitting, Computer Vision and Image Understanding, vol.63, issue.3, pp.527-541, 1996.
DOI : 10.1006/cviu.1996.0039
URL : https://hal.archives-ouvertes.fr/inria-00074483

L. Cohen, On active contour models and balloons, CVGIP: Image Understanding, vol.53, issue.2, pp.211-218, 1991.
DOI : 10.1016/1049-9660(91)90028-N
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.158.8722

L. Cohen and I. Cohen, Finite-element methods for active contour models and balloons for 2-D and 3-D images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.11, pp.1131-1147, 1993.
DOI : 10.1109/34.244675

L. D. Cohen, On active contour models and balloons, CVGIP: Image Understanding, vol.53, issue.2, pp.211-218, 1991.
DOI : 10.1016/1049-9660(91)90028-N
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.158.8722

D. Comaniciu and P. Meer, Mean shift analysis and applications, Proceedings of the Seventh IEEE International Conference on Computer Vision, p.1197, 1999.
DOI : 10.1109/ICCV.1999.790416
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.2524

D. Comaniciu and P. Meer, Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, pp.603-619, 2002.
DOI : 10.1109/34.1000236

D. Comaniciu, V. Ramesh, and P. Meer, The variable bandwidth mean shift and data-driven scale selection, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pp.438-445, 2001.
DOI : 10.1109/ICCV.2001.937550

T. Cootes, C. Beeston, G. Edwards, and C. Taylor, A Unified Framework for Atlas Matching Using Active Appearance Models, Information Processing in Medical Imaging, pp.322-333, 1999.
DOI : 10.1007/3-540-48714-X_24

T. Cootes, A. Hill, C. Taylor, and J. Haslam, Use of active shape models for locating structures in medical images, Image and Vision Computing, vol.12, issue.6, pp.355-366, 1994.
DOI : 10.1016/0262-8856(94)90060-4

T. Cootes and C. Taylor, A mixture model for representing shape variation, Image and Vision Computing, vol.17, issue.8, pp.567-574, 1999.
DOI : 10.1016/S0262-8856(98)00175-9

T. Cootes, C. Taylor, D. Cooper, and J. Graham, Training models of shape from sets of examples, British Machine Vision Conference, pp.9-18, 1992.

T. Cootes, C. Taylor, D. Cooper, and J. Graham, Active Shape Models-Their Training and Application, Computer Vision and Image Understanding, vol.61, issue.1, pp.38-59, 1995.
DOI : 10.1006/cviu.1995.1004
URL : https://www.escholar.manchester.ac.uk/api/datastream?publicationPid=uk-ac-man-scw:1d1862&datastreamId=POST-PEER-REVIEW-PUBLISHERS.PDF

D. Cremers, T. Kohlberger, and C. Schnörr, Shape statistics in kernel space for variational image segmentation, Pattern Recognition, vol.36, issue.9, pp.1929-1943, 2003.
DOI : 10.1016/S0031-3203(03)00056-6

D. Cremers, S. J. Osher, and S. Soatto, Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation, International Journal of Computer Vision, vol.127, issue.2, pp.335-351, 2006.
DOI : 10.1007/s11263-006-7533-5

D. Cremers, F. Tischhäuser, J. Weickert, and C. Schnörr, Diffusion snake : Introducing statistical shape knowledge into the mumford-shah functional, International Journal of Computer Vision, vol.3, pp.295-313, 2002.

O. Cuisenaire, Distance transformations: fast algorithm and applications to medical image processing, 1999.

E. Davies, Machine Vision: Theory, Algorithms, Practicalities, pp.171-191, 1997.

R. H. Davies, T. F. Cootes, and C. J. Taylor, A Minimum Description Length Approach to Statistical Shape Modelling, Information Processing in Medical Imaging, pp.50-63, 2001.
DOI : 10.1007/3-540-45729-1_5

C. De-boor, A practical guide to splines, 1978.
DOI : 10.1007/978-1-4612-6333-3

M. Delfour and J. Zolésio, Shapes and geometries : analysis, differential calculus, and optimization. Siam, 2001.
DOI : 10.1137/1.9780898719826

H. Delingette, General object reconstruction based on simplex meshes, International Journal of Computer Vision, vol.32, issue.2, pp.111-146, 1999.
DOI : 10.1023/A:1008157432188
URL : https://hal.archives-ouvertes.fr/inria-00073579

A. Dervieux and F. Thomasset, A finite element method for the simulation of a Rayleigh-Taylor instability, Lect. Notes Math, vol.216, pp.145-159, 1979.
DOI : 10.1016/0021-9991(79)90086-X

A. Dervieux and F. Thomasset, Multifluid incompressible flows by a finite element method, Seventh Int. Conf. on Numerical Methods in Fluid Dynamics, pp.158-163, 1980.
DOI : 10.1007/3-540-10694-4_22

G. Donato and S. Belongie, Approximate Thin Plate Spline Mappings, European Conference on Computer Vision, pp.21-31, 2002.
DOI : 10.1007/3-540-47977-5_2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.28.9160

R. Donner, M. Reiter, G. Langs, P. Peloschek, and H. Bischof, Fast Active Appearance Model Search Using Canonical Correlation Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.10, pp.1690-1694, 2006.
DOI : 10.1109/TPAMI.2006.206

D. Donoho, Compressed sensing, IEEE Transactions on Information Theory, vol.52, issue.4, pp.1289-1306, 2006.
DOI : 10.1109/TIT.2006.871582
URL : https://hal.archives-ouvertes.fr/inria-00369486

Y. Duan and H. Qin, A subdivision-based deformable model for surface reconstruction of unknown topology, Graphical Models, vol.66, issue.4, pp.181-202, 2004.
DOI : 10.1016/j.gmod.2004.05.004

J. Duchon, Splines minimizing rotation-invariant semi-norms in Sobolev spaces, Constructive theory of functions os several variables, 1977.
DOI : 10.1007/BFb0086566

R. Duda and P. Hart, Pattern Classification and Scene Analysis, 1973.

P. Etyngier, F. Ségonne, and R. Keriven, Shape Priors using Manifold Learning Techniques, 2007 IEEE 11th International Conference on Computer Vision, 2007.
DOI : 10.1109/ICCV.2007.4409040
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.120.4931

M. Figueiredo, J. M. Leitao, and A. K. Jain, On Fitting Mixture Models, Energy Minimization Methods in Computer Vision and Pattern Recognition, pp.54-69, 1999.
DOI : 10.1007/3-540-48432-9_5

C. Florin, N. Paragios, G. Funka-lea, and J. Williams, Liver Segmentation Using Sparse 3D Prior Models with Optimal Data Support, Information Processing in Medical Imaging, pp.38-49, 2007.
DOI : 10.1007/978-3-540-73273-0_4

C. Fouard and G. Malandain, 3-D chamfer distances and norms in anisotropic grids, Image and Vision Computing, vol.23, issue.2, pp.143-158, 2005.
DOI : 10.1016/j.imavis.2004.06.009
URL : https://hal.archives-ouvertes.fr/hal-00308886

A. Foulonneau, P. Charbonnier, and F. Heitz, Affine-invariant geometric shape priors for region-based active contours, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.8, pp.1352-1357, 2006.
DOI : 10.1109/TPAMI.2006.154

C. Fraley and A. Raftery, How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis, The Computer Journal, vol.41, issue.8, 1998.
DOI : 10.1093/comjnl/41.8.578

S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images*, Journal of Applied Statistics, vol.1, issue.5-6, pp.721-741, 1984.
DOI : 10.1109/TIT.1972.1054786

F. Girosi, M. Jones, and T. Poggio, Regularization Theory and Neural Networks Architectures, Neural Computation, vol.26, issue.3, pp.219-269, 1995.
DOI : 10.1016/0893-6080(90)90004-5
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.9258

J. Gomes and O. Faugeras, Reconciling Distance Functions and Level Sets, Journal of Visual Communication and Image Representation, vol.11, issue.2, pp.209-223, 2000.
DOI : 10.1006/jvci.1999.0439
URL : https://hal.archives-ouvertes.fr/inria-00073006

L. Grady, Y. Sun, and J. Williams, Three interactive graph-based segmentation methods applied to cardiovascular imaging, Handbook of Mathematical Models in Computer Vision, pp.453-469, 2006.

U. Grenander, General Pattern Theory, 1993.

A. Guéziec and N. Ayache, Smoothing and matching of 3-D space curves, International Journal of Computer Vision, vol.12, issue.1, pp.79-104, 1994.
DOI : 10.1007/3-540-55426-2_66

H. Gävert, J. Hurri, J. Särelä, and A. Hyvärinen, Fastica -fast independent component analysis, 2005.

P. Hall, T. Hui, and J. Marron, Improved variable window kernel estimates of probability densities. The Annals of Statistics, pp.1-10, 1995.

R. Haralick, Digital step edges from zero crossing of second directional derivatives, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.6, pp.58-68, 1984.

R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2000.
DOI : 10.1017/CBO9780511811685

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
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.185.1651

X. Huang, D. Metaxas, and T. Chen, Metamorphs: Deformable shape and texture models, IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, CVPR'04, pp.496-503, 2004.

X. Huang, N. Paragios, and D. Metaxas, Registration of Structures in Arbitrary Dimensions: Implicit Representations, Mutual Information & Free-Form Deformations, 2003.

X. Huang, N. Paragios, and D. N. Metaxas, Shape registration in implicit spaces using information theory and free form deformations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.8, pp.1303-1318, 2006.
DOI : 10.1109/TPAMI.2006.171
URL : https://hal.archives-ouvertes.fr/hal-00133233

P. Huber, Robust Statistics, 1981.
DOI : 10.1002/0471725250

A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis, 2001.

A. Hyvärinen and E. Oja, Independent component analysis: algorithms and applications, Neural Networks, vol.13, issue.4-5, pp.411-430, 2000.
DOI : 10.1016/S0893-6080(00)00026-5

M. Isard and A. Blake, Contour tracking by stochastic propagation of conditional density
DOI : 10.1007/BFb0015549

J. Jackson, A User's Guide to Practical Principal Components, 1991.

O. Juan, R. Keriven, and G. Postelnicu, Stochastic Motion and the Level Set Method in Computer Vision: Stochastic Active Contours, International Journal of Computer Vision, vol.21, issue.2, pp.7-25, 2006.
DOI : 10.1007/s11263-006-6849-5

K. Kanatani, Uncertainty modeling and model selection for geometric inference, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.10, pp.1307-1319, 2004.
DOI : 10.1109/TPAMI.2004.93

M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active contour models, IEEE International Conference in Computer Vision, pp.261-268, 1987.
DOI : 10.1007/BF00133570

M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active contour models, International Journal of Computer Vision, vol.5, issue.6035, pp.321-332, 1988.
DOI : 10.1007/BF00133570
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.5318

D. G. Kendall, The diffusion of shape, Advances in Applied Probability, vol.213, issue.03, pp.428-430, 1977.
DOI : 10.1017/S0001867800028743

S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi, Gradient flows and geometric active contour models, Proceedings of IEEE International Conference on Computer Vision, pp.810-815, 1995.
DOI : 10.1109/ICCV.1995.466855
URL : https://hal.archives-ouvertes.fr/hal-00002665

A. Klassen and E. Srivastava, Geometric analysis of planar shapes using geodesic paths, Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002., pp.468-472, 2002.
DOI : 10.1109/ACSSC.2002.1197226

M. Kortgen, G. J. Park, M. Novotni, and R. Klein, 3d shape matching with 3d shape contexts, the 7th Central European Seminar on Computer Graphics, 2003.

Y. Lecun, The MNIST database of handwritten digits

M. Leventon, E. Grimson, and O. Faugeras, Statistical Shape Influence in Geodesic Active Contours, IEEE Conference on Computer Vision and Pattern Recognition, pp.316-322, 2000.

F. W. Li and R. W. Lau, Real-time rendering of deformable parametric free-form surfaces, Proceedings of the ACM symposium on Virtual reality software and technology , VRST '99, pp.131-138, 1999.
DOI : 10.1145/323663.323684

S. X. Liao and M. Pawlak, On image analysis by moments, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18, issue.3, pp.254-266, 1996.
DOI : 10.1109/34.485554

E. Llorens and D. Vidal, Application of extended generalized linear discriminant functions (egldf) to optical character recognition. In Handwriting Analysis and Recognition -A European Perspective, pp.701-714, 1996.

A. Lundervold, N. Duta, T. Taxt, and A. Jain, Model-guided segmentation of corpus callosum in MR images, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), pp.1231-1238, 1999.
DOI : 10.1109/CVPR.1999.786944

J. Ma, Y. Zhao, and S. Ahalt, OSU SVM classifier matlab toolbox

J. Macqueen, Some methods for classification and analysis of multivariate, Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp.281-297, 1967.

W. R. Madych and S. A. Nelson, Polyharmonic cardinal splines: A minimization property, Journal of Approximation Theory, vol.63, issue.3, pp.303-320, 1990.
DOI : 10.1016/0021-9045(90)90123-8

G. Malandain and C. Fouard, On optimal chamfer masks and coefficients, Research Report, vol.5566, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00070440

R. Malladi, J. Sethian, and B. Vemuri, 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. Manay, D. Cremers, B. Hong, A. J. Yezzi, and S. Soatto, Integral Invariants for Shape Matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.10, pp.1602-1618, 2006.
DOI : 10.1109/TPAMI.2006.208

S. Manay, B. Hong, A. J. Yezzi, and S. Soatto, Integral Invariant Signatures, ECCV (4), pp.87-99, 2004.
DOI : 10.1007/978-3-540-24673-2_8

J. Mangin, I. Bloch, J. Lopez-krahe, and V. Frouin, Chamfer Distances in Anisotropic 3D Images, EUSIPCO 94, pp.975-978, 1994.

K. Mardia and I. Dryden, Shape distributions for landmark data, Advances in Applied Probability, vol.4, pp.742-755, 1989.

J. S. Marron and D. Nolan, Canonical kernels for density estimation, Statistics & Probability Letters, vol.7, issue.3, pp.195-199, 1988.
DOI : 10.1016/0167-7152(88)90050-8

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

T. Mcinerney and D. Terzopoulos, A dynamic finite element surface model for segmentation and tracking in multidimensional medical images with application to cardiac 4D image analysis, Computerized Medical Imaging and Graphics, vol.19, issue.1, pp.69-83, 1995.
DOI : 10.1016/0895-6111(94)00040-9

G. Mclachlan, D. Peel, and W. Whiten, Maximum likelihood clustering via normal mixture models, Signal Processing: Image Communication, pp.105-111, 1996.
DOI : 10.1016/0923-5965(95)00039-9

G. Medioni, S. Menet, and P. Saint-marc, Active contour models: Overview, implementation and application, International Conference on Systems, Man, and Cybernetics, pp.194-199, 1990.

M. Mignotte, J. Meunier, and J. Tardif, Endocardial Boundary E timation and Tracking in Echocardiographic Images using Deformable Template and Markov Random Fields, Pattern Analysis & Applications, vol.4, issue.4, pp.256-271, 2001.
DOI : 10.1007/PL00010988

S. Mika, B. Schölkopf, A. J. Smola, K. Müller, M. Scholz et al., Kernel PCA and de?noising in feature spaces, Advances in Neural Information Processing Systems 11, 1999.

J. Montagnat and H. Delingette, 4D deformable models with temporal constraints: application to 4D cardiac image segmentation, Medical Image Analysis, vol.9, issue.1, pp.87-100, 2005.
DOI : 10.1016/j.media.2004.06.025
URL : https://hal.archives-ouvertes.fr/hal-00443361

J. Montagnat, H. Delingette, and N. Ayache, A review of deformable surfaces: topology, geometry and deformation, Image and Vision Computing, vol.19, issue.14, pp.1023-1040, 2001.
DOI : 10.1016/S0262-8856(01)00064-6
URL : https://hal.archives-ouvertes.fr/inria-00615110

D. Mumford and J. Shah, Boundary detection by minimizing functionals, IEEE Conference on Computer Vision and Pattern Recognition, pp.22-26, 1985.

D. Mumford and J. Shah, Optimal approximations by piecewise smooth functions and associated variational problems, Communications on Pure and Applied Mathematics, vol.3, issue.5, pp.577-685, 1989.
DOI : 10.1002/cpa.3160420503

D. Mumford and J. Shah, Optimal approximations by piecewise smooth functions and associated variational problems, Communications on Pure and Applied Mathematics, vol.3, issue.5, pp.577-685, 1989.
DOI : 10.1002/cpa.3160420503

C. Nastar and N. Ayache, Frequency-based nonrigid motion analysis: application to four dimensional medical images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18, issue.11, pp.1067-1079, 1996.
DOI : 10.1109/34.544076
URL : https://hal.archives-ouvertes.fr/inria-00615057

S. Osher and N. Paragios, Geometric Level Set Methods in Imaging, Vision and Graphics, 2003.

S. Osher and J. 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 I. N. , Geodesic Active Regions and Level Set Methods: Contributions and Applications in Artificial Vision, 2000.

N. Paragios, Y. Chen, and O. Faugeras, Handbook of Mathematical Models in Computer Vision, 2005.
DOI : 10.1007/0-387-28831-7

N. Paragios and R. Deriche, Geodesic active regions for motion estimation and tracking, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.688-674, 1999.
DOI : 10.1109/ICCV.1999.791292
URL : https://hal.archives-ouvertes.fr/inria-00073043

N. Paragios and R. Deriche, Geodesic active regions for supervised texture segmentation, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.926-932, 1998.
DOI : 10.1109/ICCV.1999.790347
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.17.2737

N. Paragios and R. Deriche, 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

N. Paragios and R. Deriche, Geodesic Active Regions 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

N. Paragios, M. Rousson, and V. Ramesh, Matching Distance Functions: A

N. Paragios, M. Rousson, and V. Ramesh, Non-rigid registration using distance functions, Computer Vision and Image Understanding, vol.89, issue.2-3, pp.142-165, 2003.
DOI : 10.1016/S1077-3142(03)00010-9

N. Paragios, M. Taron, X. Huang, M. Rousson, and D. Metaxas, On the represantation of shapes using implicit functions, Statistics and Analysis of Shapes, pp.167-200, 2006.

E. Parzen, On Estimation of a Probability Density Function and Mode, The Annals of Mathematical Statistics, vol.33, issue.3, pp.1065-1076, 1962.
DOI : 10.1214/aoms/1177704472

X. Pennec, Multiple registration and mean rigid shape -application to the 3D case, Image Fusion and Shape Variability Techniques (16th Leeds Annual Statistical Workshop), pp.178-185, 1996.
URL : https://hal.archives-ouvertes.fr/inria-00615762

X. Pennec and J. Thirion, A framework for uncertainty and validation of 3-d registration methods based on points and frames, International Journal of Computer Vision, vol.25, issue.3, pp.203-229, 1997.
DOI : 10.1023/A:1007976002485
URL : https://hal.archives-ouvertes.fr/inria-00615070

A. Pentland and S. Sclaroff, Closed-form solutions for physically based shape modeling and recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, issue.7, pp.715-729, 1991.
DOI : 10.1109/34.85660

M. Peura and J. Iivarinen, Efficiency of simple shape descriptors, Proceedings of the Third International Workshop on visual Form, pp.443-451, 1997.

Q. Pham, F. Vincent, P. Clarysse, and P. , Croisille, and I. Magnin. A fem-based deformable model for the 3d segmentation and tracking of the heart in cardiac mri, Image and Signal Processing and Analysis, 2001.

S. M. Pizer, P. T. Fletcher, S. Joshi, A. Thall, J. Z. Chen et al., Deformable m-reps for 3d medical image segmentation, International Journal of Computer Vision, vol.55, issue.2/3, pp.85-106, 2003.
DOI : 10.1023/A:1026313132218

J. Pons and J. Boissonnat, Delaunay Deformable Models: Topology-Adaptive Meshes Based on the Restricted Delaunay Triangulation, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383019
URL : https://hal.archives-ouvertes.fr/hal-00488042

X. Provot, Deformation constraints in a mass-spring model to describe rigid cloth behavior, Graphics Interface '95, pp.147-154, 1995.

A. Rangarajan, H. Chui, and F. L. Bookstein, The softassign Procrustes matching algorithm, Information Processing in Medical Imaging, pp.29-42, 1997.
DOI : 10.1007/3-540-63046-5_3
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.1387

J. Rissanen, Stochastic Complexity in Statistical Inquiry, 1989.
DOI : 10.1142/0822

M. Rochery, I. H. Jermyn, and J. Zerubia, Phase field models and higher-order active contours, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005.
DOI : 10.1109/ICCV.2005.189
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.218.5983

K. Rohr, M. Fornefett, and H. S. Stiehl, Spline-based elastic image registration: integration of landmark errors and orientation attributes, Computer Vision and Image Understanding, vol.90, issue.2, pp.153-168, 2003.
DOI : 10.1016/S1077-3142(03)00048-1

F. Rosenblatt, Remarks on Some Nonparametric Estimates of a Density Function, The Annals of Mathematical Statistics, vol.27, issue.3, pp.832-837, 1956.
DOI : 10.1214/aoms/1177728190

A. Rosenfeld and J. Pfaltz, Sequential operations in digital picture processing, Journal of the Association for Computing Machinery, vol.13, issue.4, pp.471-494, 1966.

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., pp.699-704, 2003.
DOI : 10.1109/CVPR.2003.1211535
URL : https://hal.archives-ouvertes.fr/inria-00071891

M. Rousson and N. Paragios, Shape Priors for Level Set Representations, European Conference on Computer Vision, pages II, pp.78-93, 2002.
DOI : 10.1007/3-540-47967-8_6
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.4415

M. Rousson and N. Paragios, Prior Knowledge, Level Set Representations & Visual Grouping, International Journal of Computer Vision, vol.18, issue.3
DOI : 10.1007/s11263-007-0054-z
URL : https://hal.archives-ouvertes.fr/hal-00918700

M. Rousson, N. Paragios, and R. Deriche, Implicit active shape models for 3d segmentationin mri imaging, International Conference on Medical Image Computing and Computer Assisted Intervention, 2004.

E. Rouy and A. Tourin, A Viscosity Solutions Approach to Shape-From-Shading, SIAM Journal on Numerical Analysis, vol.29, issue.3, pp.867-884, 1992.
DOI : 10.1137/0729053

D. Rueckert, L. Sonoda, C. Hayes, D. Hill, M. Leach et al., Nonrigid registration using free-form deformations: application to breast MR images, IEEE Transactions on Medical Imaging, vol.18, issue.8, pp.712-721, 1999.
DOI : 10.1109/42.796284
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.126.4732

S. Sain and D. Scott, On Locally Adaptive Density Estimation, Journal of the American Statistical Association, vol.8, issue.436, pp.1525-1534, 1996.
DOI : 10.2307/2290569
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.7339

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

B. Schölkopf and A. J. Smola, A Short Introduction to Learning with Kernels, Proceedings of the Machine Learning Summer School, pp.41-64, 2003.
DOI : 10.1007/3-540-36434-X_2

D. W. Scott, Multivariate Density Estimation: Theory, Practice, and Visualization, 1992.
DOI : 10.1002/9781118575574

T. Sederberg and S. Parry, Free-form deformation of solid geometric models, Proceedings SIGGRAPH '86, pp.151-160, 1986.

J. Sethian, A Review of the Theory, Algorithms, and Applications of Level Set Methods for Propagating Interfaces, pp.487-499, 1995.

J. Sethian, Level Set Methods, 1996.

J. Sethian, Fast Marching Methods, SIAM Review, vol.41, issue.2, pp.199-235, 1999.
DOI : 10.1137/S0036144598347059

E. Simoncelli, Bayesian multi-scale differential optical flow, Handbook of Computer Vision and Applications, pp.397-422, 1999.

S. Soatto and A. J. Yezzi, DEFORMOTION Deforming Motion, Shape Average and the Joint Registration and Segmentation of Images, ECCV (3), pp.32-57, 2002.
DOI : 10.1007/3-540-47977-5_3

M. Sofka, G. Yang, and C. V. Stewart, Simultaneous Covariance Driven Correspondence (CDC) and Transformation Estimation in the Expectation Maximization Framework, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.18-23, 2007.
DOI : 10.1109/CVPR.2007.383166

J. Solem, Variational Problems and Level Set Methods in Computer Vision -Theory and Applications, 2006.

L. Staib and S. Duncan, Boundary finding with parametrically deformable models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.11, pp.1061-1075, 1992.
DOI : 10.1109/34.166621
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.835

L. H. Staib and J. S. Duncan, Model-based deformable surface finding for medical images, IEEE Transactions on Medical Imaging, vol.15, issue.5, pp.720-731, 1996.
DOI : 10.1109/42.538949
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.3187

C. Stewart, Covariance-based registration, 2002.

C. Stewart, The Handbook of Mathematical Models in Computer Vision, chapter Uncertainty-Driven Point-Based Image Registration, 2005.

C. Stewart, C. Tsai, and B. Roysam, The dual-bootstrap iterative closest point algorithm with application to retinal image registration, IEEE Transactions on Medical Imaging, vol.22, issue.11, pp.1379-1394, 2003.
DOI : 10.1109/TMI.2003.819276

C. V. Stewart, Robust Parameter Estimation in Computer Vision, SIAM Review, vol.41, issue.3, pp.513-537, 1999.
DOI : 10.1137/S0036144598345802

G. Sundaramoorthi, A. Yezzi, and A. Mennucci, Sobolev active contours, IEEE International Conference in Computer Vision, pp.345-366, 2007.
DOI : 10.1007/s11263-006-0635-2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.121.7504

G. Szekely, A. Kelemen, C. Brechbuhler, and G. Gerig, Segmentation of 3d objects from mri volume data using constrained elastic deformations of flexible fourier surface models, Computer Vision, p.95, 1995.

R. Szeliski, D. Tonnesen, and D. Terzopoulos, Modeling surfaces of arbitrary topology with dynamic particles, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp.82-87, 1993.
DOI : 10.1109/CVPR.1993.340975

J. Tarel and D. B. Cooper, A new complex basis for implicit polynomial curves and its simple exploitation for pose estimation and invariant recognition, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231), pp.111-117, 1998.
DOI : 10.1109/CVPR.1998.698596

M. Taron, C. Ghys, and N. Paragios, Uncertainties-driven Surface Morphing: The case of Photo-realistic Transitions between Facial Expressions, 18th International Conference on Pattern Recognition (ICPR'06), pp.889-892, 2006.
DOI : 10.1109/ICPR.2006.1140

M. Taron, N. Paragios, and M. Jolly, Modelling shapes with uncertainties: higher order polynomials, variable bandwidth kernels and non parametric density estimation, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.1659-1666, 2005.
DOI : 10.1109/ICCV.2005.153
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.3602

M. Taron, N. Paragios, and M. Jolly, Uncertainty-driven non-parametric knowledgebased segmentation: The corpus callosum case, IEEE Workshop in Variational and Level Set Methods, pp.198-207, 2005.

M. Taron, N. Paragios, and M. Jolly, From Uncertainties to Statistical Model Building and Segmentation of the Left Ventricle, 2007 IEEE 11th International Conference on Computer Vision, 2007.
DOI : 10.1109/ICCV.2007.4409129

M. Taron, N. Paragios, and M. Jolly, Registration with Uncertainties and Statistical Modeling of Shapes with Variable Metric Kernels, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.1
DOI : 10.1109/TPAMI.2008.36
URL : https://hal.archives-ouvertes.fr/hal-00712244

T. Tasdizen, J. Tarel, and D. Cooper, Improving the stability of algebraic curves for applications, IEEE Transactions on Image Processing, vol.9, issue.3, pp.405-416, 2000.
DOI : 10.1109/83.826778

G. Taubin, F. Cukierman, S. Sullivan, J. Ponce, and D. J. Kriegman, Parameterized families of polynomials for bounded algebraic curve and surface fitting, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.16, issue.3, pp.287-303, 1994.
DOI : 10.1109/34.276128

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

D. Terzopoulos, On Matching Deformable Models to Images, 1986.

D. Terzopoulos and K. Fleischer, Deformable Models. The Visual Computer, pp.306-331, 1988.

D. Terzopoulos and D. Metaxas, Dynamic 3D models with local and global deformations: deformable superquadrics, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, issue.7, pp.703-714, 1991.
DOI : 10.1109/34.85659

P. Thévenaz and M. Unser, Precision isosurface rendering of 3-D image data, IEEE Transactions on Image Processing, vol.12, issue.7, pp.764-775, 2003.
DOI : 10.1109/TIP.2003.814240

D. W. Thompson, On Growth and Form: The Complete Revised Edition, 1917.

A. Tikhonov, Ill-Posed Problems in Natural Sciences, Coronet, 1992.

M. E. Tipping and C. M. Bishop, Probabilistic Principal Component Analysis, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.61, issue.3, pp.611-622, 1999.
DOI : 10.1111/1467-9868.00196

A. Tsai, A. Yezzi, W. Wells, C. Tempany, D. Tucker et al., Model-based curve evolution technique for image segmentation, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp.463-468, 2001.
DOI : 10.1109/CVPR.2001.990511

G. Turk, J. Obrien, ]. M. Uzümcü, A. Frangi, J. Reiber et al., Shape Transformation Using Variational Implicit Functions Independent component analysis in statistical shape models, ACM SIGGRAPH SPIE Medical Imaging, pp.335-342, 1999.

M. Vasilescu and D. Terzopoulos, Adaptive meshes and shells: irregular triangulation, discontinuities, and hierarchical subdivision, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.829-832, 1992.
DOI : 10.1109/CVPR.1992.223247
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.8192

B. C. Vemuri, S. M. Rahman, and J. Li, Multiresolution adaptive K-means algorithm for segmentation of brain MRI, ICSC '95: Proceedings of the Third International Computer Science Conference on Image Analysis Applications and Computer Graphics, pp.347-354, 1995.
DOI : 10.1007/3-540-60697-1_121

B. J. Verwer, Local distances for distance transformations in two and three dimensions, Pattern Recognition Letters, vol.12, issue.11, pp.671-682, 1991.
DOI : 10.1016/0167-8655(91)90004-6

L. Vese and T. Chan, A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model, International Journal of Computer Vision, vol.50, issue.3, pp.271-293, 2002.
DOI : 10.1023/A:1020874308076

P. Viola and W. Wells, Aligment by Maximization of Mutual Information, IEEE International Conference in Computer Vision, pp.16-23, 1995.

C. Walder, O. Chapelle, and B. Schölkopf, Implicit surface modelling as an eigenvalue problem, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.936-939, 2005.
DOI : 10.1145/1102351.1102469
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.79.2274

M. Wand and M. Jones, Kernel Smoothing, 1995.
DOI : 10.1007/978-1-4899-4493-1

W. Welch and A. Witkin, Free-form shape design using triangulated surfaces, Proceedings of the 21st annual conference on Computer graphics and interactive techniques , SIGGRAPH '94, pp.247-256, 1994.
DOI : 10.1145/192161.192216
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.25.6352

W. Wells, R. Kikinis, W. Grimson, and F. Jolesz, Adaptive segmentation of MRI data, IEEE Transactions on Medical Imaging, vol.15, issue.4, pp.429-442, 1996.
DOI : 10.1109/42.511747

D. Werman and D. Weinshall, Similarity and affine invariant distances between 2D point sets, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.17, issue.8, pp.810-814, 1995.
DOI : 10.1109/34.400572

Z. Xie and G. Farin, Deformation with hierarchical b-splines, Mathematical Methods for Curves and Surfaces, pp.545-554, 2001.

Z. Xie and G. E. Farin, Image registration using hierarchical b-splines, IEEE Trans. Vis. Comput. Graph, vol.10, issue.1, pp.85-94, 2004.

L. Yatziv, A. Bartesaghi, and G. Sapiro, O(N) implementation of the fast marching algorithm, Journal of Computational Physics, vol.212, issue.2, pp.393-399, 2006.
DOI : 10.1016/j.jcp.2005.08.005

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. Yoshida, B. Keserci, and K. Doi, Computer-aided diagnosis of pulmonary nodules in chest radiographs: a wavelet-based snake approach, Proceedings. 11th IEEE Symposium on Computer-Based Medical Systems (Cat. No.98CB36237), pp.258-263, 1998.
DOI : 10.1109/CBMS.1998.701368

A. Yuille, P. Hallinan, and D. Cohen, Feature extraction from faces using deformable templates, International Journal of Computer Vision, vol.26, issue.6, pp.99-111, 1992.
DOI : 10.1007/BF00127169

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

Y. Zhang, M. Brady, and S. Smith, Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm, IEEE Transactions on Medical Imaging, vol.20, issue.1, pp.45-57, 2001.
DOI : 10.1109/42.906424

Z. Zhang, Iterative point matching for registration of free-form curves and surfaces, International Journal of Computer Vision, vol.7, issue.3, pp.119-152, 1994.
DOI : 10.1007/BF01427149

S. Zhu, T. Lee, 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.416-423, 1995.
DOI : 10.1109/ICCV.1995.466909

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

W. Zhu, T. Chan, and U. , Stability for shape comparison model Publication of the author Journal: ? Registration with Uncertainties and Statistical Modeling of Shapes with Variable Metric Kernels, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003.

B. Chapter, ? On the Represantation of Shapes Using Implicit Functions