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1 October 1999 Multivariate discriminant-analysis-based algorithm for distortion-invariant image recognition
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Haisong Liu, Qingsheng He, Minxian Wu, Guofan Jin, and Yingbai Yan "Multivariate discriminant-analysis-based algorithm for distortion-invariant image recognition", Proc. SPIE 3804, Algorithms, Devices, and Systems for Optical Information Processing III, (1 October 1999);

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