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Proceedings Paper

Physiologically motivated computational visual target recognition beta selection
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Paper Abstract

This paper investigates the use of a beta value derived from a receiver operator characteristic curve for target recognition. Using a physiologically-motivated sensor-fusion algorithm, lower-level data is filtered and fused using a pulse-coupled neural network (PCNN) to represent the feature processing of the parvocellular and magnetocellular pathways. High level decision making includes feature association from the PCNN filter, information fusion, and selection of a signal-detection beta value that optimizes performance. A beta value is represent bias based on a likelihood ratio of Gaussian distributions that can be used as a decision strategy to discriminate between targets. By employing a beta value as the output of the physiologic- motivated sensor fusion algorithm, targets are classified based on the fusion of feature data.

Paper Details

Date Published: 30 March 2000
PDF: 10 pages
Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); doi: 10.1117/12.380598
Show Author Affiliations
Erik P. Blasch, Air Force Research Lab. (United States)
Randy P. Broussard, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 4055:
Applications and Science of Computational Intelligence III
Kevin L. Priddy; Paul E. Keller; David B. Fogel, Editor(s)

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