Statistical shape model of legendre moments with active contour evolution for shape detection and segmentation

Authors:
Yan Zhang;Bogdan J. Matuszewski;Aymeric Histace;Frédéric Precioso
Affiliations:
ADSIP Research Centre, University of Central Lancashire, UK;ADSIP Research Centre, University of Central Lancashire, UK;ETIS Lab, CNRS/ENSEA/Univ Cergy-Pontoise, Cergy-Pontoise, France;ETIS Lab, CNRS/ENSEA/Univ Cergy-Pontoise, Cergy-Pontoise, France and LIP6 UMR CNRC 7606, UPMC Sorbonne Universités, Paris, France
Venue:
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Year:
2011

Citing 7
Cited 1

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Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation

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Abstract

This paper describes a novel method for shape detection and image segmentation. The proposed method combines statistical shape models and active contours implemented in a level set framework. The shape detection is achieved by minimizing the Gibbs energy of the posterior probability function. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. The proposed energy is minimized by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results are also presented to show that the proposed method has very robust performances for images with a large amount of noise.