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Proceedings Paper

Feature Selection and Decision Space Mapping for Sensor Fusion
Author(s): Cynthia L. Beer; Gerald M. Flachs; David R. Scott; Jay B. Jordan
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Paper Abstract

An information fusion approach is presented for mapping a multiple dimensional feature space into a lower dimensional decision space with simplified decision boundaries. A new statistic, called the tie statistic, is used to perform the mapping by measuring differences in probability density functions of features. These features are then evaluated based on the separation of the decision classes using a parametric beta representation for the tie statistic. The feature evaluation and fusion methods are applied to perform texture recognition.

Paper Details

Date Published: 1 March 1990
PDF: 12 pages
Proc. SPIE 1198, Sensor Fusion II: Human and Machine Strategies, (1 March 1990); doi: 10.1117/12.969978
Show Author Affiliations
Cynthia L. Beer, New Mexico State University (United States)
Gerald M. Flachs, New Mexico State University (United States)
David R. Scott, New Mexico State University (United States)
Jay B. Jordan, New Mexico State University (United States)


Published in SPIE Proceedings Vol. 1198:
Sensor Fusion II: Human and Machine Strategies
Paul S. Schenker, Editor(s)

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