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Pré-Publication, Document De Travail Année : 2009

Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model

Résumé

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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Dates et versions

hal-00250375 , version 1 (11-02-2008)
hal-00250375 , version 2 (16-01-2009)

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Citer

Stéphanie Allassonnière, Estelle Kuhn. Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model. 2009. ⟨hal-00250375v2⟩
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