Unsupervised segmentation based on Von Mises circular distributions for orientation estimation in textured images

Abstract : In the case of textured images and more particularly of directional textures, a new parametric technique is proposed to estimate the orientation field of textures. It consists of segmenting the image into regions with homogeneous orientations, and estimating the orientation inside each of these regions. This allows us to maximize the size of the samples used to estimate the orientation without being corrupted by the presence of boundaries between regions. For that purpose, the local--hence noisy--orientations of the texture are first estimated using small filters (3×3 pixels). The segmentation of the obtained orientation field image then relies on a generalization of a minimum description length based segmentation technique, to the case of π-periodic circular data modeled with Von Mises probability density functions. This leads to a fast segmentation algorithm without tuning parameters in the optimized criterion. The accuracy of the orientations estimated with the proposed method is then compared with other approaches on synthetic images and an application to the processing of real images is finally addressed.
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Submitted on : Thursday, May 24, 2012 - 12:11:22 PM
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Jean-Pierre Da Costa, Frédéric Galland, Antoine Roueff, Christian Germain. Unsupervised segmentation based on Von Mises circular distributions for orientation estimation in textured images. Journal of Electronic Imaging, SPIE and IS&T, 2012, 21 (2), pp.021102. ⟨10.1117/1.JEI.21.2.021102⟩. ⟨hal-00700943⟩

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