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.
Type de document :
Article dans une revue
Journal of Mathematical Imaging and Vision, Springer Verlag, 2013, 47 (1), pp.35-47. <10.1007/s10851-013-0416-9>
https://hal.archives-ouvertes.fr/hal-00784159
Contributeur : Aymeric Histace <>
Soumis le : dimanche 3 février 2013 - 21:04:43
Dernière modification le : lundi 13 octobre 2014 - 15:43:25
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>
