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Proceedings Paper

Assumption truth maintenance in model-based ATR algorithm design
Author(s): Laura Fulton Bennett; Rubin Johnson; Cecil Ivan Hudson
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Paper Abstract

In a given approach to automatic target recognition (ATR) algorithm design, an underlying network of assumptions provides computational and conceptual efficiency. This network includes concrete assumptions about the physical characteristics of the real-world scene and abstract assumptions about knowledge acquisition and representation. A facility for the identification and tracking of assumptions in dynamic systems is critical for algorithm design and performance evaluation purposes. The intersection of assumptions at a designated stage of the target recognition process defines the valid domain of application of the ATR system. An approach to assumption truth maintenance for application to complex, visual pattern recognition systems is described. The types of assumptions made in key-feature, model-based ATR systems are systematically identified, from the low-level pixel domain to the high-level mission statement. The approach permits the tracking of algorithm assumptions as they propagate through the pattern recognition process and provides for belief formation and revision to maintain consistency. The approach is demonstrated on a prototype set of infrared test imagery at varying levels of resolution and signal-to-noise ratio, representative of the given problem domain.

Paper Details

Date Published: 1 August 1991
PDF: 12 pages
Proc. SPIE 1470, Data Structures and Target Classification, (1 August 1991); doi: 10.1117/12.44857
Show Author Affiliations
Laura Fulton Bennett, U.S. Army Ctr. for Night Vision and Electro-Optics (United States)
Rubin Johnson, OR Concepts Applied (United States)
Cecil Ivan Hudson, Expersoft (United States)

Published in SPIE Proceedings Vol. 1470:
Data Structures and Target Classification
Vibeke Libby, Editor(s)

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