Active Contours Based Battachryya Gradient Flow for Texture Segmentation

We present a new unsupervised segmentation of textural images based on integration of texture descriptor in formulation of active contour. The proposed texture descriptor intrinsically describes the geometry of textural regions using the shape operator defined in Beltrami framework. We use Battachryya distance to define an active contour model which discriminates textures by maximizing distance between the probability density functions which leads to distinguish textural objects of interest and background described by texture descriptor. We prove the existence of a solution to the new formulated active contour based segmentation model and we propose a fast and easy way to implement texture segmentation algorithm based on the dual formulation of the Total Variation norm. Finally, we show results on challenging images to illustrate accurate segmentations that are possible.