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Optical Engineering

Iterative estimation of Dempster-Shafer's basic probability assignment: application to multisensor image segmentation
Author(s): Fabien Salzenstein; Abdel-Ouahab Boudraa
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Paper Abstract

Basic probability assignment (BPA) definition remains a difficult problem to apply Desmpter-Shafer evidence theory to practical applications such as in image processing. A new iterative approach of multisensor data fusion based on the Dempster-Shafer framework is proposed. BPAs, modeled by a Gaussian distribution, are estimated iteratively and in an unsupervised way using the fused image and the source images. Data fusion is performed at the pixel level. Results on synthetic and real images are presented to illustrate the effectiveness of the proposed fusion scheme. Limitations of the method are discussed.

Paper Details

Date Published: 1 June 2004
PDF: 7 pages
Opt. Eng. 43(6) doi: 10.1117/1.1737373
Published in: Optical Engineering Volume 43, Issue 6
Show Author Affiliations
Fabien Salzenstein, Univ. de Strasbourg (France)
Abdel-Ouahab Boudraa, Ecole Navale (France)

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