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Unsupervised texture segmentation using a nonlinear energy optimization method

Lookup NU author(s): Dr Sasan Mahmoodi, Professor Bayan Sharif


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A nonlinear functional is considered for segmentation of images containing structural textures. A structural texture pattern in in 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. © 2006 SPIE and IS&T.

Publication metadata

Author(s): Mahmoodi S, Sharif BS

Publication type: Article

Publication status: Published

Journal: Journal of Electronic Imaging

Year: 2006

Volume: 15

Issue: 3

ISSN (print): 1017-9909

ISSN (electronic):

Publisher: SPIE: International Society for Optical Engineering


DOI: 10.1117/1.2234370

Notes: Article no. 033006


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