Bimodal texture segmentation with the Lee-Seo model

Authors:
Michalis A. Savelonas;Dimitris K. Iakovidis;Dimitris Maroulis
Affiliations:
Dept. of Informatics and Telecommunications, University of Athens, Athens, Greece;Dept. of Informatics and Telecommunications, University of Athens, Athens, Greece;Dept. of Informatics and Telecommunications, University of Athens, Athens, Greece
Venue:
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Year:
2007

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Abstract

This paper presents a novel approach to bimodal texture segmentation. The proposed approach features a local binary pattern-based scheme to transform bimodal textures into bimodal gray-scale intensities, segmentable by the Lee-Seo active contour model. This process avoids the iterative calculation of active contour equation terms derived from textural feature vectors, thus reducing the associated computational overhead. The proposed approach is region-based and invariant to the initialization of the level-set function, as it converges to a stationary global minimum. It is experimentally validated on 18 composite texture images of the Brodatz album, obtaining high quality segmentation results, whereas the convergence times are up to an order of magnitude smaller than the ones reported for other active contour approaches for texture segmentation.