Fast unsupervised texture segmentation using active contours model driven by Bhattacharyya gradient flow

Abstract

We present a new unsupervised segmentation based active contours model and texture descriptor. The proposed texture descriptor intrinsically describes the geometry of textural regions using the shape operator defined in Beltrami framework. We use Bhattacharyya distance to discriminate textures by maximizing distance between the probability density functions which leads to distinguish textural objects of interest and background. We propose a fast Bregman split implementation of our segmentation algorithm based on the dual formulation of the Total Variation norm. Finally, we show results on some challenging images to illustrate segmentations that are possible. © 2009 Springer-Verlag Berlin Heidelberg.