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

Yan Zhang 1 Bogdan Matuszewski 1 Aymeric Histace 2, * Frédéric Precioso 3
* Corresponding author
ETIS - Equipes Traitement de l'Information et Systèmes
3 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe KEIA
Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : This paper describes a novelmethod for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. 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. A greedy strategy is applied to optimize the proposed cost function 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 presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data.
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Contributor : Aymeric Histace <>
Submitted on : Sunday, February 3, 2013 - 9:04:43 PM
Last modification on : Monday, November 5, 2018 - 3:52:10 PM

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Yan Zhang, Bogdan Matuszewski, Aymeric Histace, Frédéric Precioso. Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation. Journal of Mathematical Imaging and Vision, Springer Verlag, 2013, 47 (1), pp.35-47. ⟨10.1007/s10851-013-0416-9⟩. ⟨hal-00784159⟩



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