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TOWARD A COHERENT STATISTICAL FRAMEWORK FOR DENSE DEFORMABLE TEMPLATE ESTIMATION
 

Summary: TOWARD A COHERENT STATISTICAL FRAMEWORK FOR DENSE
DEFORMABLE TEMPLATE ESTIMATION
S. ALLASSONNI`ERE, Y. AMIT, A. TROUV´E
Abstract. 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 images of deformable objects. We propose
a rigorous Bayesian framework for which we can derived an iterative algorithm for the
effective estimation of the geometric and photometric parameters of the model in a small
sample setting, together with an asymptotic consistency proof. The model is extended
to mixtures of finite numbers of such components leading to a fine description of the
photometric and geometric variations. We illustrate some of the ideas with images of
handwritten digits, and apply the estimated models to classification through maximum
likelihood.
1. Introduction
Modeling the geometric variability of object classes with deformable templates has
proved to be a powerful tool in image analysis. Important applications can be found
in general object detection and recognition problems in vision, ([6], [12], [1], [4]), where in
addition to explicit modeling of geometric variability the deformable template framework

  

Source: Amit, Yali - Departments of Computer Science & Statistics, University of Chicago

 

Collections: Computer Technologies and Information Sciences