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30 March 2000 Physiologically motivated computational visual target recognition beta selection
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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.
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Erik P. Blasch and Randy P. Broussard "Physiologically motivated computational visual target recognition beta selection", Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000);

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