Using the Dempster-Shafer reasoning model to perform pixel-level segmentation on color images

Matt G. Payne, Qiuming Zhu, Yinghua Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherPubl by Int Soc for Optical Engineering
Pages26-35
Number of pages10
ISBN (Print)0819409391
StatePublished - 1992
EventNeural and Stochastic Methods in Image and Signal Processing - San Diego, CA, USA
Duration: Jul 20 1992Jul 23 1992

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume1766
ISSN (Print)0277-786X

Other

OtherNeural and Stochastic Methods in Image and Signal Processing
CitySan Diego, CA, USA
Period7/20/927/23/92

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Using the Dempster-Shafer reasoning model to perform pixel-level segmentation on color images'. Together they form a unique fingerprint.

Cite this