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Proceedings Paper

Optical AI Approach For Object Recognition Using Dempster-Shafer Theory Of Evidence
Author(s): M. H. Hassan; P. Siy
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Paper Abstract

The problem of object recognition in simple and complex images, where the information about the scene may be incomplete, uncertain, or imprecise, is addressed. The general outline of the proposed approach consists of four stages: (a) Preprocessing, to convert raw data into more usable intrinsic forms. (b) Segmentation, to find visually meaningful image objects perhaps corresponding to world objects or their parts. This processing is used to define a region in the image models by a set of attributes. The set of attributes represents the ordered set of vertices of the polygonal approximation of the region. Each vertex is defined by its internal angle and the lengths of segments around it. (c) Optical mapping, to find the appropriate representation of the image optically. (d) Optical understanding, to relate optically the image objects to the domain from which the image arose. As a step of this process the evidences related to each object with corresponding models are evaluated based on Dempster-Shafer theory of evidence. The approach could be implemented on optoelectronic processing unit using the fan-in and fan-out capabilities of fiber-optics system. Illustrative examples are provided where the approach yields excellent results in noisy images and images with overlapping objects.

Paper Details

Date Published: 16 December 1989
PDF: 12 pages
Proc. SPIE 0977, Real-Time Signal Processing XI, (16 December 1989); doi: 10.1117/12.948556
Show Author Affiliations
M. H. Hassan, Lawrence Institute of Technology (United States)
P. Siy, Wayne State University (United States)


Published in SPIE Proceedings Vol. 0977:
Real-Time Signal Processing XI
J. P. Letellier, Editor(s)

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