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

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.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-00624357
Contributor : Aymeric Histace <>
Submitted on : Friday, September 16, 2011 - 4:01:24 PM
Last modification on : Monday, July 1, 2019 - 3:42:13 PM

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  • HAL Id : hal-00624357, version 1

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Yan Zhang, Bogdan Matuszewski, Aymeric Histace, Frédéric Precioso. Statistical Shape Model of Legendre Moments with Active Contour Evolution for Shape Detection and Segmentation. Computer Analysis of Images and Patterns, Aug 2011, Sevilla, Spain. pp.51-58. ⟨hal-00624357⟩

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