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PublisherThe University of Arizona.
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AbstractOptical matched filters have been used for the recognition of patterns in a noisy background. Different types of matched filters have been proposed since the introduction of the VanderLugt matched spatial filter. A novel filter, the hybrid phase-only matched filter, is proposed which shows promise for better signal to noise ratio, correlation peak intensity and light efficiency compared to the recently proposed optimal phase-only filter. A neural technique for the design of space-domain binary filter for pattern recognition applications is developed. The method takes advantage of the similarity in the structure of the minimum squared error criterion for the construction of linear discriminant functions and the Lyapunov function of the Hopfield Neural Model.