Share Email Print

Proceedings Paper

Using the Dempster-Shafer reasoning model to perform pixel-level segmentation on color images
Author(s): Matt G. Payne; Quiming Zhu; Yinghua Huang
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Dempster-Shafer's theory of evidence is a generalization of Bayes reasoning that allows multiple information sources with varying levels of belief to contribute to probabilistic decisions. We present an algorithm that performs pixel-level segmentation based upon the Dempster-Shafer theory of evidence. The algorithm fuses image data from the multichannels of color spectra. Dempster-Shafer reasoning is used to drive the evidence accumulation process for pixel level segmentation of color scenes. Experiments are presented that use spectral information from the RGB and HSI color models to segment a color image with Dempster-Shafer reasoning. These experiments begin to point out the utility and pitfalls of using Dempster-Shafer reasoning for segmenting color images.

Paper Details

Date Published: 16 December 1992
PDF: 10 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130814
Show Author Affiliations
Matt G. Payne, Univ. of Nebraska/Omaha (United States)
Quiming Zhu, Univ. of Nebraska/Omaha (United States)
Yinghua Huang, Univ. of Nebraska/Omaha (United States)

Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

© SPIE. Terms of Use
Back to Top