Journal Published Online: 08 May 2007
Volume 4, Issue 4

Utilization of Dempster-Shafer Theory of Evidence in Unsupervised Image Segmentation

CODEN: JAIOAD

Abstract

In this paper, we propose a new method based on the Dempster-Shafer theory of evidence for unsupervised image segmentation. This theory turns out to be quite efficient in classification of multisensor information. The application of the evidence theory in fusing information coming from different sources still poses certain problems. Of paramount importance is the problem of estimating the belief functions. Due to the coherence of this theory with the Bayesian approach, a parametric algorithm to estimate these functions based on the maximum likelihood method can be realized to estimate these belief functions. The proposed method is validated by experiments on both synthetic and real images. The experimental results show the interest of the algorithm and its potential.

Author Information

Zribi, M.
Université du Littoral Côte d’Opale, Calais Cedex, France
Rekik, A.
Université du Littoral Côte d’Opale, Calais Cedex, France
Benjelloun, M.
Université du Littoral Côte d’Opale, Calais Cedex, France
Pages: 13
Price: $25.00
Related
Reprints and Permissions
Reprints and copyright permissions can be requested through the
Copyright Clearance Center
Details
Stock #: JAI100658
ISSN: 1546-962X
DOI: 10.1520/JAI100658