Open Access

Colour Image Segmentation Using Homogeneity Method and Data Fusion Techniques

  • Salim Ben Chaabane1Email author,
  • Mounir Sayadi1, 2,
  • Farhat Fnaiech1, 2 and
  • Eric Brassart2
EURASIP Journal on Advances in Signal Processing20092010:367297

https://doi.org/10.1155/2010/367297

Received: 17 December 2008

Accepted: 11 May 2009

Published: 16 June 2009

Abstract

A novel method of colour image segmentation based on fuzzy homogeneity and data fusion techniques is presented. The general idea of mass function estimation in the Dempster-Shafer evidence theory of the histogram is extended to the homogeneity domain. The fuzzy homogeneity vector is used to determine the fuzzy region in each primitive colour, whereas, the evidence theory is employed to merge different data sources in order to increase the quality of the information and to obtain an optimal segmented image. Segmentation results from the proposed method are validated and the classification accuracy for the test data available is evaluated, and then a comparative study versus existing techniques is presented. The experimental results demonstrate the superiority of introducing the fuzzy homogeneity method in evidence theory for image segmentation.

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Authors’ Affiliations

(1)
SICISI Unit, High school of sciences and techniques of Tunis (ESSTT)
(2)
Laboratory for Innovation Technologies (LTI-UPRES EA3899), Electrical Power Engineering Group (EESA), University of Picardie Jules Verne

Copyright

© Salim Ben Chaabane et al. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.