Abstract
This article addresses the development of and recent advances in the rapidly growing field of optical pattern recognition. In optical pattern recognition there are two basic approaches; namely, matched filtering and associative memories. The first employs optical correlator architectures and the latter uses optical neural networks (NNs). This paper reviews various types of optical correlators and NNs applied to real-time pattern recognition and autonomous tracking. Techniques of scale and rotational invariant filtering are also given. Recent approaches using wavelet transform filtering, phase only filtering, high capacity composite filters and phase representation for improvement in pattern discrimination are also provided.
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