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The basic properties of nonlinear global filtering techniques are analyzed. A nonlinear processor for pattern recognition that is optimum in terms of discrimination and that is tolerant of variations of the object to be recognized is presented. We compare this processor with power-law and nonlinear joint transform correlators.

We develop a processor for pattern recognition that is optimum in terms of discrimination and is tolerant to variations of the object to be recognized. This optimum processor is found to be adaptive nonlinear joint-transform correlator.