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Summary: Statistical Shape Modeling using Morphological Representations
Santiago Velasco-Forero and Jes´us Angulo
Centre de Morphologie Math´ematique, Math´ematiques et Syst`emes, MINES ParisTech
E-mail: santiago.velasco / jesus.angulo@mines-paristech.fr
Abstract
The aim of this paper is to propose tools for statisti-
cal analysis of shape families using morphological op-
erators. Given a series of shape families (or shape cate-
gories), the approach consists in empirically computing
shape statistics (i.e., mean shape and variance of shape)
and then to use simple algorithms for random shape
generation, for empirical shape confidence boundaries
computation and for shape classification using Bayes
rules. The main required ingredients for the present
methods are well known in image processing, such as
watershed on distance functions or log-polar transfor-
mation. Performance of classification is presented in a
well-known shape database.
1. Introduction
Object recognition based on shape information is a
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