Abstract
Optical pattern recognition can be improved using powerful filters or defining new correlations. The morphological correlation is a robust detection method that minimizes the mean absolute error between two patterns. The morphological correlation is a nonlinear correlation and it is defined as the average over all the amplitudes of the linear correlation between thresholded versions of the input scene and the reference object for every gray level. This nonlinear correlation can be implemented optically using a joint transform correlator and provides higher performance and higher discrimination abilities in comparison with other linear correlation methods. We define different morphological correlations using different binary decompositions. Those correlations allow efficient pattern recognition with higher discrimination ability than other common linear image detection techniques. Experimental result will be presented.
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