Voir la notice de l'article provenant de la source Numdam
The estimation of probabilistic deformable template models in computer vision or of probabilistic atlases in Computational Anatomy are core issues in both fields. A first coherent statistical framework where the geometrical variability is modelled as a hidden random variable has been given by [S. Allassonnière et al., J. Roy. Stat. Soc. 69 (2007) 3-29]. They introduce a bayesian approach and mixture of them to estimate deformable template models. A consistent stochastic algorithm has been introduced in [S. Allassonnière et al. (in revision)] to face the problem encountered in [S. Allassonnière et al., J. Roy. Stat. Soc. 69 (2007) 3-29] for the convergence of the estimation algorithm for the one component model in the presence of noise. We propose here to go on in this direction of using some “SAEM-like” algorithm to approximate the MAP estimator in the general bayesian setting of mixture of deformable template models. We also prove the convergence of our algorithm toward a critical point of the penalised likelihood of the observations and illustrate this with handwritten digit images and medical images.
@article{PS_2010__14__382_0, author = {Allassonni\`ere, St\'ephanie and Kuhn, Estelle}, title = {Stochastic algorithm for bayesian mixture effect template estimation}, journal = {ESAIM: Probability and Statistics}, pages = {382--408}, publisher = {EDP-Sciences}, volume = {14}, year = {2010}, doi = {10.1051/ps/2009001}, language = {en}, url = {http://geodesic.mathdoc.fr/articles/10.1051/ps/2009001/} }
TY - JOUR AU - Allassonnière, Stéphanie AU - Kuhn, Estelle TI - Stochastic algorithm for bayesian mixture effect template estimation JO - ESAIM: Probability and Statistics PY - 2010 SP - 382 EP - 408 VL - 14 PB - EDP-Sciences UR - http://geodesic.mathdoc.fr/articles/10.1051/ps/2009001/ DO - 10.1051/ps/2009001 LA - en ID - PS_2010__14__382_0 ER -
%0 Journal Article %A Allassonnière, Stéphanie %A Kuhn, Estelle %T Stochastic algorithm for bayesian mixture effect template estimation %J ESAIM: Probability and Statistics %D 2010 %P 382-408 %V 14 %I EDP-Sciences %U http://geodesic.mathdoc.fr/articles/10.1051/ps/2009001/ %R 10.1051/ps/2009001 %G en %F PS_2010__14__382_0
Allassonnière, Stéphanie; Kuhn, Estelle. Stochastic algorithm for bayesian mixture effect template estimation. ESAIM: Probability and Statistics, Tome 14 (2010), pp. 382-408. doi : 10.1051/ps/2009001. http://geodesic.mathdoc.fr/articles/10.1051/ps/2009001/
[1] Toward a coherent statistical framework for dense deformable template estimation. J. Roy. Stat. Soc. 69 (2007) 3-29.
, and ,[2] Map estimation of statistical deformable templates via nonlinear mixed effects models: Deterministic and stochastic approaches. In Proc. Int. Workshop on the Mathematical Foundations of Computational Anatomy (MFCA-2008), edited by X. Pennec and S. Joshi (2008).
, and ,[3] Construction of Bayesian deformable models via a stochastic approximation algorithm: A convergence study. Bernoulli 16 (2010) 641-678.
, and ,[4] Structural image restoration through deformable templates. J. Am. Statist. Assoc. 86 (1989) 376-387.
, and ,[5] Stability of stochastic approximation under verifiable conditions. SIAM J. Control Optim. 44 (2005) 283-312 (electronic). | Zbl
, and ,[6] Actives appearance models. In 5th Eur. Conf. on Computer Vision, Berlin, Vol. 2, edited by H. Burkhards and B. Neumann. Springer (1998) 484-498.
, and ,[7] Convergence of a stochastic approximation version of the EM algorithm. Ann. Statist. 27 (1999) 94-128. | Zbl
, and ,[8] Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. 1 (1977) 1-22. | Zbl
, and ,[9] Nonparametric density estimation in hidden Markov models. Statist. Inf. Stoch. Process. 5 (2002) 55-64. | Zbl
and ,[10] A penalised likelihood approach to image warping. J. Roy. Statist. Soc., Ser. B 63 (2001) 465-492. | Zbl
and ,[11] Template estimation form unlabeled point set data and surfaces for computational anatomy. In Proc. Int. Workshop on the Mathematical Foundations of Computational Anatomy (MFCA-2006), edited by X. Pennec and S. Joshi (2006) 29-39.
and ,[12] General Pattern Theory. Oxford Science Publications (1993). | Zbl
,[13] Martingale limit theory and its application. Probab. Math. Statist. Academic Press Inc. [Harcourt Brace Jovanovich Publishers], New York (1980). | Zbl
and ,[14] Coupling a stochastic approximation version of EM with an MCMC procedure. ESAIM: PS 8 (2004) 115-131 (electronic). | Zbl | mathdoc-id
and ,[15] Stochastic approximation methods for constrained and unconstrained systems, volume 26 of Appl. Math. Sci. Springer-Verlag, New York (1978). | Zbl
and ,[16] A minimum description length objective function for groupwise non rigid image registration. Image and Vision Computing (2007).
, and ,[17] Markov chains and stochastic stability. Communications and Control Engineering Series. Springer-Verlag, London Ltd. (1993). | Zbl
and ,[18] T.A. and L. Younes, On the metrics and Euler-Lagrange equations of computational anatomy. Ann. Rev. Biomed. Eng. 4 (2002) 375-405.
,[19] Méthodes de Monte Carlo par chaînes de Markov. Statistique Mathématique et Probabilité. [Mathematical Statistics and Probability]. Éditions Économica, Paris (1996). | Zbl
,[20] Statistics on diffeomorphisms via tangent space representations. Neuroimage 23 (2004) S161-S169.
, , and ,Cité par Sources :