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

Image classification by an optical implementation of the Fukunaga–Koontz transform

James R. Leger and Sing H. Lee
J. Opt. Soc. Am. 72(5) 556-564 (1982)

Recognition of images of Markov-1 model by least-squares linear mapping technique

Zu-Han Gu and Sing H. Lee
Appl. Opt. 23(6) 822-827 (1984)

Comparison of statistical pattern-recognition algorithms for hybrid processing. I. Linear-mapping algorithms

Q. Tian, Y. Fainman, Z. H. Gu, and Sing H. Lee
J. Opt. Soc. Am. A 5(10) 1655-1669 (1988)

Classification of multiclassed stochastic images buried in additive noise

Zu-Han Gu and Sing H. Lee
J. Opt. Soc. Am. A 4(4) 712-719 (1987)

Hybrid optical processor for pattern recognition and classification using a generalized set of pattern functions

James R. Leger and Sing H. Lee
Appl. Opt. 21(2) 274-287 (1982)