Optical AI Approach For Object Recognition Using Dempster-Shafer Theory Of Evidence

M. H. Hassan; P. Siy

doi:10.1117/12.948556

16 December 1989 Optical AI Approach For Object Recognition Using Dempster-Shafer Theory Of Evidence

M. H. Hassan, P. Siy

Author Affiliations +

Proceedings Volume 0977, Real-Time Signal Processing XI; (1989) https://doi.org/10.1117/12.948556
Event: 32nd Annual International Technical Symposium on Optical and Optoelectronic Applied Science and Engineering, 1988, San Diego, CA, United States

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.

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M. H. Hassan and P. Siy "Optical AI Approach For Object Recognition Using Dempster-Shafer Theory Of Evidence", Proc. SPIE 0977, Real-Time Signal Processing XI, (16 December 1989); https://doi.org/10.1117/12.948556

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