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Proceedings Paper

Expanded Dempster-Shafer reasoning technique for image feature integration and object recognition
Author(s): Quiming Zhu; Yinghua Huang; Matt G. Payne
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Paper Abstract

Integration of information from multiple sources has been one of the key steps to the success of general vision systems. It is also an essential problem to the development of color image understanding algorithms that make full use of the multichannel color data for object recognition. This paper presents a feature integration system characterized by a hybrid combination of a statistic-based reasoning technique and a symbolic logic-based inference method. A competitive evidence enhancement scheme is used in the process to fuse information from multiple sources. The scheme expands the Dempster-Shafer's function of combination and improves the reliability of the object recognition. When applied to integrate the object features extracted from the multiple spectra of the color images, the system alleviates the drawback of traditional Baysian classification system.

Paper Details

Date Published: 16 December 1992
PDF: 12 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130815
Show Author Affiliations
Quiming Zhu, Univ. of Nebraska/Omaha (United States)
Yinghua Huang, Univ. of Nebraska/Omaha (United States)
Matt G. Payne, 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)

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