Active Contours Based Battachryya Gradient Flow for Texture Segmentation

• Published in: 2009 2nd International Congress on Image and Signal Processing pp. 1 - 6
• Main Authors: Derraz, F., Taleb-Ahmed, A., Peyrodie, L., Pinti, A., Chikh, A., Bereksi-Reguig, F.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2009
• Subjects: Active contours; Feature extraction; Gabor filters; Image segmentation; Probability density function; Prototypes; Solid modeling; Statistical analysis; Tensile stress; Testing
• ISBN: 1424441293; 9781424441297
• DOI: 10.1109/CISP.2009.5304339

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.