Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model

Details Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model

2008-02-11

arxiv.org/abs/0802.1521v2

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Computation

Scientific paper

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 Allassonni\`ere, Amit and Trouv\'e 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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