Proceedings PaperDistortion Invariant Pattern Recognition By Nonlinear Optical Correlation
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We describe a training image correlation based pattern recognition system using a nonlinear processor to perform distortion invariant pattern recognition. The training image set is stored at the input plane of the optical processor side by side the input scene. The nonlinear processor operates by producing the joint power spectrum of the training image set and the input scene. A thresholding nonlinearity is applied to the joint power spectrum of the input scene and the reference images. For binary nonlinear optical processors, both the input signals and the joint power spectrum are binarized to only two values. Thus, a binary spatial light modulator can be used to implement the processor. The performance of the training image based nonlinear processor is compared to that of the conventional linear optical processors. For binary nonlinear optical processors, a single binary spatial light modulator can be used to read in sequentially the binarized input signals and the binarized joint power spectrum which results in a reduction in cost and complexity of the system.