CONSTRUCTION OF BAYESIAN DEFORMABLE MODELS VIA STOCHASTIC APPROXIMATION ALGORITHM: A CONVERGENCE STUDY - Centre de mathématiques appliquées (CMAP) Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2008

CONSTRUCTION OF BAYESIAN DEFORMABLE MODELS VIA STOCHASTIC APPROXIMATION ALGORITHM: A CONVERGENCE STUDY

Résumé

The problem of the definition and the estimation of generative models based on deformable templates from raw data is of particular importance for modelling non aligned data affected by various types of geometrical variability. This is especially true in shape modelling in the computer vision community or in probabilistic atlas building for Computational Anatomy (CA). A first coherent statistical framework modelling the geometrical variability as hidden variables has been given by Allassonnière, Amit and Trouvé (JRSS 2006). Setting the problem in a Bayesian context they proved the consistency of the MAP estimator and provided a simple iterative deterministic algorithm with an EM flavour leading to some reasonable approximations of the MAP estimator under low noise conditions. In this paper we present a stochastic algorithm for approximating the MAP estimator in the spirit of the SAEM algorithm. We prove its convergence to a critical point of the observed likelihood with an illustration on images of handwritten digits.
Fichier principal
Vignette du fichier
aktdefmodHAL.pdf (867.3 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00151999 , version 1 (06-06-2007)
hal-00151999 , version 2 (16-01-2009)

Identifiants

Citer

Stéphanie Allassonnière, Estelle Kuhn, Alain Trouvé. CONSTRUCTION OF BAYESIAN DEFORMABLE MODELS VIA STOCHASTIC APPROXIMATION ALGORITHM: A CONVERGENCE STUDY. 2008. ⟨hal-00151999v2⟩
351 Consultations
222 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More