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The estimation of probabilistic deformable template models in
computer vision or of probabilistic atlases in Computational Anatomy
are core issues in both fields.
A first coherent statistical framework where the geometrical variability is
modelled as a hidden
random variable has been
given by [S. Allassonnière ,
(2007) 3–29]. They introduce
a Bayesian approach and
mixture of them to estimate deformable template models.
A consistent stochastic algorithm has been introduced in [S. Allassonnière ...
High dimensional data are more and more frequent in many application fields. It becomes particularly important to be able to extract meaningful features from these data sets. Deformable template model is a popular way to achieve this. This paper is a review on the statistical aspects of this model as well as its generalizations. We describe the different mathematical frameworks to handle different data types as well as the deformations. We recall the theoretical convergence properties of the estimators...
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