Abstract

Utilizing the phase-coded optical processor, the least-squares linear mapping technique (LSLMT) has been optically implemented to classify large-dimensional images. The LSLMT is useful for performing a transform from large-dimensional observation or feature space to small-dimensional decision space for separating multiple image classes by maximizing the interclass differences while minimizing the intraclass variations. As an example, the classifier designed for handwritten letters was studied. The performance of the LSLMT was compared also with those of a matched filter and an average filter.

© 1982 Optical Society of America

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