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

This paper describes a novel method 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.     

Constructive action of noise for impulsive noise removal in scalar images

A nonlinear variational approach to remove impulsive noise in scalar images is proposed. Taking benefit from recent studies on the use of stochastic resonance and the constructive role of noise in nonlinear processes, the process is based on the classical restoration process of Perona-Malik in which a Gaussian noise is purposely injected. It is shown that this new process can outperform the original restoration process of Perona-Malik.