Unsupervised texture segmentation using active contours driven by the Chernoff gradient flow

Authors:
Foued Derraz;Abdelmalik Taleb-Ahmed;Nacim Betrouni;Azzeddine Chikh;Antonio Pinti;Fethi Bereksi-Reguig
Affiliations:
LAMIH, UMR, CNRS, Valenciennes, France and Genie Biomedical Laboratory, Abou Bekr Belkaid University, Tlemcen, Algeria;LAMIH, UMR, CNRS, Valenciennes, France;INSERM, University hospital of Lille, France;Genie Biomedical Laboratory, Abou Bekr Belkaid University, Tlemcen, Algeria;LAMIH, UMR, CNRS, Valenciennes, France;Genie Biomedical Laboratory, Abou Bekr Belkaid University, Tlemcen, Algeria
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

We present a new unsupervised segmentation of textural images based on integration of a texture descriptor in the 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 the Chernoff distance to define an active contours model which discriminates textures by maximizing the 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 contours based segmentation model and we propose a fast and easy 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.