Unsupervised texture segmentation using a nonlinear energy optimization method

Sasan Mahmoodi; Bayan S. Sharif

doi:10.1117/1.2234370

1 July 2006 Unsupervised texture segmentation using a nonlinear energy optimization method

Sasan Mahmoodi, Bayan S. Sharif

Author Affiliations +

Journal of Electronic Imaging, Vol. 15, Issue 3, 033006 (July 2006). https://doi.org/10.1117/1.2234370

Abstract

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.

©(2006) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Sasan Mahmoodi and Bayan S. Sharif "Unsupervised texture segmentation using a nonlinear energy optimization method," Journal of Electronic Imaging 15(3), 033006 (1 July 2006). https://doi.org/10.1117/1.2234370

Published: 1 July 2006

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