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Proceedings Paper

Uncertainty Management In A Rule-Based Automatic Target Recognizer
Author(s): James M. Keller; Gregory Hobson
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Paper Abstract

Automatic target recognition (ATR) is one of the most challenging tasks for a computer vision system. It involves the determination of objects in natural scenes in different weather conditions and in the presence of both active and passive countermeasures and battlefield contaminants. This high degree of variability introduces considerable uncertainty into the vision processes in an ATR. This mandates both a flexible control structure capable of adapting as conditions change and a method for managing the uncertainty to aggregate evidence. The desired flexibility can be achieved with a rule-based system in which the knowledge of the effects of scene content and ancillary information on algorithm choices and parameter values can be modeled and manipulated. In this paper, we describe such a system. The uncertainty is modelled by a combination of fuzzy set theory and Dempster-Shafer belief theory. Several variations of these methodologies within the rule-based structure are explored. The results are compared using sequences of forward looking infrared images.

Paper Details

Date Published: 21 March 1989
PDF: 12 pages
Proc. SPIE 1095, Applications of Artificial Intelligence VII, (21 March 1989); doi: 10.1117/12.969265
Show Author Affiliations
James M. Keller, University of Missouri-Columbia (United States)
Gregory Hobson, Emerson Electric (United States)

Published in SPIE Proceedings Vol. 1095:
Applications of Artificial Intelligence VII
Mohan M. Trivedi, Editor(s)

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