Fast Unsupervised Texture Segmentation Using Active Contours Model Driven by Bhattacharyya Gradient Flow

Authors:
Foued Derraz;Abdelmalik Taleb-Ahmed;Antonio Pinti;Laurent Peyrodie;Nacim Betrouni;Azzeddine Chikh;Fethi Bereksi-Reguig
Affiliations:
LAMIH UMR CNRS 8530, Valenciennes, France 59313 and GBM Laboratory, Abou Bekr Belkaid university, Tlemcen, Algeria 13000;LAMIH UMR CNRS 8530, Valenciennes, France 59313;LAMIH UMR CNRS 8530, Valenciennes, France 59313;Hautes Etudes d'Ingénieur Lille, France;LAMIH UMR CNRS 8530, Valenciennes 59313;GBM Laboratory, Abou Bekr Belkaid university, Tlemcen, Algeria 13000;GBM Laboratory, Abou Bekr Belkaid university, Tlemcen, Algeria 13000
Venue:
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2009

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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.