Unsupervised texture segmentation using a nonlinear energy optimization method

Sasan Mahmoodi, Bayan S. Sharif

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageBritish English
Article number033006
JournalJournal of Electronic Imaging
Volume15
Issue number3
DOIs
StatePublished - Jul 2006

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