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9 November 1993 Reduced classification error probability with ternary correlation filters
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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.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
John D. Downie "Reduced classification error probability with ternary correlation filters", Proc. SPIE 2026, Photonics for Processors, Neural Networks, and Memories, (9 November 1993);


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