Feature Selection and Decision Space Mapping for Sensor Fusion

Cynthia L. Beer; Gerald M. Flachs; David R. Scott; Jay B. Jordan

doi:10.1117/12.969978

1 March 1990 Feature Selection and Decision Space Mapping for Sensor Fusion

Cynthia L. Beer, Gerald M. Flachs, David R. Scott, Jay B. Jordan

Proceedings Volume 1198, Sensor Fusion II: Human and Machine Strategies; (1990) https://doi.org/10.1117/12.969978
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States

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.

Citation Download Citation

Cynthia L. Beer, Gerald M. Flachs, David R. Scott, and Jay B. Jordan "Feature Selection and Decision Space Mapping for Sensor Fusion", Proc. SPIE 1198, Sensor Fusion II: Human and Machine Strategies, (1 March 1990); https://doi.org/10.1117/12.969978

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