Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model

Stéphanie Allassonnière; Estelle Kuhn

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arXiv · 0802.1521

Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model

Stéphanie Allassonnière·Estelle Kuhn

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Abstract

Estimating probabilistic deformable template models is a new approach in the fields of computer vision and probabilistic atlases in computational anatomy. A first coherent statistical framework modelling the variability as a hidden random variable has been given by Allassonnière, Amit and Trouvé in [1] in simple and mixture of deformable template models. A consistent stochastic algorithm has been introduced in [2] to face the problem encountered in [1] 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 model. We also prove the convergence of this algorithm toward a critical point of the penalised likelihood of the observations and illustrate this with handwritten digit images.

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BibTeX RIS

Stéphanie Allassonnière, Estelle Kuhn. 2009-01-16. Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model. https://arxiv.org/abs/0802.1521

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