Segmentation of Vectorial Image Features Using Shape Gradients and Information Measures

In this paper, we propose to focus on the segmentation of vectorial features (e.g. vector fields or color intensity) using region-based active contours. We search for a domain that minimizes a criterion based on homogeneity measures of the vectorial features. We choose to evaluate, within each region to be segmented, the average quantity of information carried out by the vectorial features, namely the joint entropy of vector components. We do not make any assumption on the underlying distribution of joint probability density functions of vector components, and so we evaluate the entropy using non parametric probability density functions. A local shape minimizer is then obtained through the evolution of a deformable domain in the direction of the shape gradient. The first contribution of this paper lies in the methodological approach used to differentiate such a criterion. This approach is mainly based on shape optimization tools. The second one is the extension of this method to vector...

• Type: Journal
• Year: 2006
• Where: JMIV
• Authors: Ariane Herbulot, Stéphanie Jehan-Besson, Stefan Duffner, Michel Barlaud, Gilles Aubert
• Keywords: JMIV 2006, Probability Density Functions, Vector Fields, Vectorial Features