Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model
| dc.creator | Allassonnière, Stéphanie | |
| dc.creator | Kuhn, Estelle | |
| dc.date | 2008-02-11 | |
| dc.date | 2009-01-16 | |
| dc.date.accessioned | 2026-07-07T12:29:59Z | |
| dc.date.available | 2026-07-07T12:29:59Z | |
| dc.description | 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. | |
| dc.identifier | https://arxiv.org/abs/0802.1521 | |
| dc.identifier | http://arxiv.org/abs/0802.1521 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/216016 | |
| dc.subject | Computation | |
| dc.subject | Statistics Theory | |
| dc.title | Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model | |
| dc.type | text |