Optical implementation of distortion-invariant pattern recognition based on multivariate statistical methods

Haisong Liu; Minxian Wu; Guofan Jin; Qingsheng He; Yingbai Yan

doi:10.1117/12.341329

9 March 1999 Optical implementation of distortion-invariant pattern recognition based on multivariate statistical methods

Haisong Liu, Minxian Wu, Guofan Jin, Qingsheng He, Yingbai Yan

Author Affiliations +

Proceedings Volume 3715, Optical Pattern Recognition X; (1999) https://doi.org/10.1117/12.341329
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

In this paper, we incorporate the multivariate statistical methods into an incoherent optical correlator based optoelectronic pattern recognition system and realize the distortion-invariant recognition. In this approach, a set of eigenimages are first extracted from a large number of training images including various typical distortions by using the principal component analysis and then are used as the reference patterns in the correlator. The optical correlation results between the testing image and the set of eigenimages construct a feature space, on which the multivariate discriminant analysis is performed. During both the training and the classification process, a bifurcating tree structure is used, by which the recognition speed of the system can be greatly improved.

Citation Download Citation

Haisong Liu, Minxian Wu, Guofan Jin, Qingsheng He, and Yingbai Yan "Optical implementation of distortion-invariant pattern recognition based on multivariate statistical methods", Proc. SPIE 3715, Optical Pattern Recognition X, (9 March 1999); https://doi.org/10.1117/12.341329

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