Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2008

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.
Fichier principal
Vignette du fichier
saemmultiHal.pdf (499.4 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

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

Identifiants

Citer

Stéphanie Allassonniere, Estelle Kuhn. Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model. 2008. ⟨hal-00250375v1⟩
324 Consultations
117 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More