Properties of the entropy function encountered in physics and information theory are employed in the generation of highly selective spatial filters for pattern recognition. Computer simulations and laboratory experiments demonstrate efficient recognition of single patterns or classes even when these are submerged in high level random noise.

When optical correlators are used for target location measurement or tracking, errors in the position estimation may be induced by the presence of noise and additional objects in the field of view. The variance of the positioning error is evaluated with its dependence on the capabilities of the spatial filter to discriminate among the patterns in the observation field, the shape of the correlation functions, and the noise.

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