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Unsupervised texture segmentation using a nonlinear energy optimization method

[+] Author Affiliations
Sasan Mahmoodi

Newcastle University,School of Biology and Psychology, Psychology Division, Henry Wellcome Building, Framlington Place, Newcastle upon Tyne, NE2 4HH, United Kingdom

Bayan S. Sharif

Newcastle University,School of Electrical, Electronic, and Computer Engineering, Merz Court, Newcastle upon Tyne, NE1 7RU, United Kingdom

J. Electron. Imaging. 15(3), 033006 (July 24, 2006). doi:10.1117/1.2234370
History: Revised October 24, 2005; Revised March 28, 2006; Accepted April 17, 2006; Published July 24, 2006
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A nonlinear functional is considered for segmentation of images containing structural textures. A structural texture pattern in an image is characterized by a certain amplitude spectrum, and segmentation of different patterns is obtained by detecting different regions with different amplitude spectra. A gradient-descent-based algorithm is proposed by deriving equations minimizing the functional. This algorithm, implementing the solutions minimizing the functional, is based on the level set method. An effective method employed in this algorithm is shown to be robust in a noisy environment. Experimental results demonstrate that the proposed method outperforms segmentation obtained by using the simulated annealing algorithm based on Gaussian Markov random fields.

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© 2006 SPIE and IS&T

Citation

Sasan Mahmoodi and Bayan S. Sharif
"Unsupervised texture segmentation using a nonlinear energy optimization method", J. Electron. Imaging. 15(3), 033006 (July 24, 2006). ; http://dx.doi.org/10.1117/1.2234370


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