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Geometrical-based algorithm for variational segmentation and smoothing of vector-valued images

Geometrical-based algorithm for variational segmentation and smoothing of vector-valued images
Geometrical-based algorithm for variational segmentation and smoothing of vector-valued images
An optimisation method based on a nonlinear functional is considered for segmentation and smoothing of vector-valued images. An edge-based approach is proposed to initially segment the image using geometrical properties such as metric tensor of the linearly smoothed image. The nonlinear functional is then minimised for each segmented region to yield the smoothed image. The functional is characterised with a unique solution in contrast with the Mumford–Shah functional for vector-valued images. An operator for edge detection is introduced as a result of this unique solution. This operator is analytically calculated and its detection performance and localisation are then compared with those of the DroGoperator. The implementations are applied on colour images as examples of vector-valued images, and the results demonstrate robust performance in noisy environments.
112-122
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 (2007) Geometrical-based algorithm for variational segmentation and smoothing of vector-valued images. IET Image Processing, 1 (2), 112-122.

Record type: Article

Abstract

An optimisation method based on a nonlinear functional is considered for segmentation and smoothing of vector-valued images. An edge-based approach is proposed to initially segment the image using geometrical properties such as metric tensor of the linearly smoothed image. The nonlinear functional is then minimised for each segmented region to yield the smoothed image. The functional is characterised with a unique solution in contrast with the Mumford–Shah functional for vector-valued images. An operator for edge detection is introduced as a result of this unique solution. This operator is analytically calculated and its detection performance and localisation are then compared with those of the DroGoperator. The implementations are applied on colour images as examples of vector-valued images, and the results demonstrate robust performance in noisy environments.

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Published date: April 2007
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 265876
URI: http://eprints.soton.ac.uk/id/eprint/265876
PURE UUID: 0d8fe15d-2473-4ebc-b6ae-1f9c6f276f40

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Date deposited: 10 Jun 2008 09:48
Last modified: 19 Jul 2019 22:22

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Contributors

Author: Sasan Mahmoodi
Author: Bayan Sharif

University divisions

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