By Topic

Using Dempster-Shafer Theory to Fuse Multiple Information Sources in Region-Based Segmentation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Adamek, T. ; Dublin City Univ., Dublin ; O'Connor, N.E.

This paper presents a new method for segmentation of images into large regions that reflect the real world objects present in a scene. It explores the feasibility of utilizing spatial configuration of regions and their geometric properties (the so-called syntactic visual features by C. Ferran Bennstrom and JR Casas (2004)) for improving the correspondence of segmentation results produced by the well-known recursive shortest spanning tree (RSST) algorithm by O.J. Morris et al. (1986) to semantic objects present in the scene. The main contribution of this paper is a novel framework for integration of evidence from multiple sources with the region merging process based on the Dempster-Shafer (DS) theory by P. Smets (1988) that allows integration of sources providing evidence with different accuracy and reliability. Extensive experiments indicate that the proposed solution limits formation of regions spanning more than one semantic object.

Published in:

Image Processing, 2007. ICIP 2007. IEEE International Conference on  (Volume:2 )

Date of Conference:

Sept. 16 2007-Oct. 19 2007