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Journal of Electronic Imaging

Unsupervised segmentation based on Von Mises circular distributions for orientation estimation in textured images
Author(s): Jean-Pierre Da Costa; Christian Germain; Frederic Galland; Antoine Roueff
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Paper 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.

Paper Details

Date Published: 7 May 2012
PDF: 8 pages
J. Electron. Imag. 21(2) 021102 doi: 10.1117/1.JEI.21.2.021102
Published in: Journal of Electronic Imaging Volume 21, Issue 2
Show Author Affiliations
Jean-Pierre Da Costa, Univ. Bordeaux 1 (France)
Christian Germain, Univ. Bordeaux 1 (France)
Frederic Galland, Institut Fresnel (France)
Antoine Roueff, Institut Fresnel (France)

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