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Proceedings Paper

Multivariate discriminant-analysis-based algorithm for distortion-invariant image recognition
Author(s): Haisong Liu; Qingsheng He; Minxian Wu; Guofan Jin; Yingbai Yan
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Paper Abstract

In this paper, we combined the multivariate discriminant analysis into an optical correlator and thus improved the distortion-invariant recognition ability of the processor. In this approach, a set of eigenimages are first extracted from a large number of training images including various distortions by using the K-L transform and then are used as the reference images in the optical correlator. The correlation results between the testing image and the set of eigenimages construct a feature space, on which the multivariate discriminant analysis is performed. As a result, a set of low dimensional discriminant vectors representing each image in the training set will be obtained and saved in memory during the training process. When any testing image with unknown membership inputs, it will be processed with the same operations and gets its discriminant vector. Using the simple minimal distance rule, the testing image can be classified into a group whose discriminant vector approximates that of the testing image most. Because the images in the training set are selected to representing all the typical distortions in each group, the algorithm can deal with the distortions to a large extent.

Paper Details

Date Published: 1 October 1999
PDF: 8 pages
Proc. SPIE 3804, Algorithms, Devices, and Systems for Optical Information Processing III, (1 October 1999); doi: 10.1117/12.363970
Show Author Affiliations
Haisong Liu, Tsinghua Univ. (United States)
Qingsheng He, Tsinghua Univ. (China)
Minxian Wu, Tsinghua Univ. (China)
Guofan Jin, Tsinghua Univ. (China)
Yingbai Yan, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 3804:
Algorithms, Devices, and Systems for Optical Information Processing III
Bahram Javidi; Demetri Psaltis, Editor(s)

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