Volume 14, 2010
|Page(s)||382 - 408|
|Published online||22 December 2010|
Stochastic algorithm for Bayesian mixture effect template estimation
CMAP - École polytechnique, Route de Saclay, 91128 Palaiseau, France; Allassoniere@gmail.com
2 LAGA - Université Paris 13, 99 av. J.-B. Clément, 93430 Villetaneuse, France and INRA - Unité MIA, Domaine de Vilvert, 78352 Jouy-en-Josas, France
Revised: 16 January 2009
Revised: 19 March 2009
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.
Mathematics Subject Classification: 60J22 / 62F10 / 62F15 / 62M40
Key words: Stochastic approximations / non rigid-deformable templates / shapes statistics / MAP estimation / Bayesian method / mixture models
© EDP Sciences, SMAI, 2010
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.