Uncertainty Management In A Rule-Based Automatic Target Recognizer

James M. Keller; Gregory Hobson

doi:10.1117/12.969265

21 March 1989 Uncertainty Management In A Rule-Based Automatic Target Recognizer

James M. Keller, Gregory Hobson

Proceedings Volume 1095, Applications of Artificial Intelligence VII; (1989) https://doi.org/10.1117/12.969265
Event: SPIE 1989 Technical Symposium on Aerospace Sensing, 1989, Orlando, FL, United States

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.

Citation Download Citation

James M. Keller and Gregory Hobson "Uncertainty Management In A Rule-Based Automatic Target Recognizer", Proc. SPIE 1095, Applications of Artificial Intelligence VII, (21 March 1989); https://doi.org/10.1117/12.969265

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