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Proceedings Paper

Knowledge-based pattern recognition using an associative processor
Author(s): Arun D. Kulkarni; Vijay B. Nagpurkar
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Paper Abstract

An image understanding system often consists of the preprocessing, feature extraction, and classification stages. In this paper we have considered a descriptive approach for the classification. As an illustration, we have considered the problem of identification of sailing crafts. The properties like the position of the mast(s), height of the mast, and type of sails are used as features. The classification scheme is described by a hierarchical tree structure. We have created the knowledge base for the classifier by encoding the classification rules, using an associative processor. A number of operations can be performed with the associative processor. They include: an upward closure, downward closure, union, and intersection. In order to use the processor as a classifier, the intersection has been used. The intersection is achieved by performing a downward closure followed by thresholding. We have used a two- layer nonlinear feedback network as the associative processor. We have also developed a menu-driven input/output interface for the classifier.

Paper Details

Date Published: 20 April 1993
PDF: 9 pages
Proc. SPIE 1827, Model-Based Vision, (20 April 1993); doi: 10.1117/12.143064
Show Author Affiliations
Arun D. Kulkarni, Univ. of Texas/Tyler (United States)
Vijay B. Nagpurkar, Univ. of Texas/Tyler (United States)

Published in SPIE Proceedings Vol. 1827:
Model-Based Vision
Hatem N. Nasr; Rodney M. Larson, Editor(s)

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