@inproceedings{713a700ef91d4179af88d6fc2a226b48,
title = "Using the Dempster-Shafer reasoning model to perform pixel-level segmentation on color images",
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.",
author = "Payne, {Matt G.} and Qiuming Zhu and Yinghua Huang",
year = "1992",
language = "English (US)",
isbn = "0819409391",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "Publ by Int Soc for Optical Engineering",
pages = "26--35",
booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",
note = "Neural and Stochastic Methods in Image and Signal Processing ; Conference date: 20-07-1992 Through 23-07-1992",
}