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Proceedings Paper

Reduced classification error probability with ternary correlation filters
Author(s): John D. Downie
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Paper Abstract

Much of the filter design work that has been performed to date for filter SLMs with both constrained and unconstrained modulation characteristics has been concerned with optimizing the design for certain performance criteria associated only with the correlation function of the target image. However, in most likely application scenarios there will be multiple objects that may populate the field of view, and the most important correlation performance criterion is ultimately the probability of correct classification of a given object as either belonging to the in-class set or the out-of-class set. In this work, we study the problem of designing ternary phase and amplitude filters (TPAFs) that reduce the probability of image misclassification. We use the Fisher ratio as a measure of the correct classification rate, and we attempt to maximize this quantity in our filter designs. Given the nonanalytical nature of the design problem, we employ a simulated annealing optimization technique. We present computer simulation results for several cases including single in-class and out-of-class image sets and multiple image sets corresponding to the design of synthetic discriminant function filters. We find significant reductions in expected rates of classification error in comparison to BPOFs and other TPAF designs.

Paper Details

Date Published: 9 November 1993
PDF: 12 pages
Proc. SPIE 2026, Photonics for Processors, Neural Networks, and Memories, (9 November 1993); doi: 10.1117/12.163573
Show Author Affiliations
John D. Downie, NASA Ames Research Ctr. (United States)


Published in SPIE Proceedings Vol. 2026:
Photonics for Processors, Neural Networks, and Memories
Stephen T. Kowel; William J. Miceli; Joseph L. Horner; Bahram Javidi; Stephen T. Kowel; William J. Miceli, Editor(s)

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