Towards a coherent statistical framework for dense deformable template estimation

Toward a Coherent Statistical Framework for Dense Deformable Template Estimation

The problem of estimating probabilistic deformable template models in the field of computer vision or of probabilistic atlases in the field of computational anatomy has not yet received a coherent statistical formulation and remains a challenge. In this paper, we provide a careful definition and analysis of a well defined statistical model based on dense deformable templates for gray level imag...

Parameter estimation for a deformable template model

SUMMARY High–level image analysis is an area which has seen considerable research interest in the past few years. In particular the task of object recognition has been successfully tackled using deformable template models in a Bayesian context. However these models are not being widely used in practise , and one of the reasons for this may be that they do require a number of rather non-intuitiv...

Towards Automatic Generation of a Coherent TTCN-3 Template Framework

The template framework is a subset of TTCN-3 template declarations, which determines the entire template hierarchy within an abstract test suite. This paper introduces a deterministic method fortest suite. This paper introduces a deterministic method for generating such a template framework from type definitions.

Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model

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 be...

Statistical Analysis of Deformable Template Models for Medical Image Understanding