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Unsupervised Texture Segmentation Using a nonlinear Energy Optimisation

Unsupervised Texture Segmentation Using a nonlinear Energy Optimisation
Unsupervised Texture Segmentation Using a nonlinear Energy Optimisation
A nonlinear functional is considered in this paper 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 in this paper by deriving equations minimising the functional. This algorithm implementing the solutions minimising 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
33006
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Sharif, Bayan
d57a4cae-a6f0-4ab3-b2d8-ef594a75857f
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Sharif, Bayan
d57a4cae-a6f0-4ab3-b2d8-ef594a75857f

Mahmoodi, Sasan and Sharif, Bayan (2006) Unsupervised Texture Segmentation Using a nonlinear Energy Optimisation. Journal of Electronic Imaging, 15 (3), 33006.

Record type: Article

Abstract

A nonlinear functional is considered in this paper 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 in this paper by deriving equations minimising the functional. This algorithm implementing the solutions minimising 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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Published date: July 2006
Organisations: Southampton Wireless Group

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Local EPrints ID: 265873
URI: http://eprints.soton.ac.uk/id/eprint/265873
PURE UUID: 8ca46447-1d75-4de1-ac5c-c1127bbfc624

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Date deposited: 10 Jun 2008 09:19
Last modified: 24 Sep 2020 16:41

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Contributors

Author: Sasan Mahmoodi
Author: Bayan Sharif

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