Abstract
The coherent optical filtering techniques provide a general concept for the classification of patterns. This paper describes the design and testing of a hybrid optical-digital image processing system and the development of methods for a statistical expansion of the correlation signals. A conventional correlation signal intensity measurement is in most of the applications not sufficient. Six different algorithms for correlation signal evaluation are investigated. A feature reduction is achieved by multivariate analysis. For alpha-numeric patterns distored by binary random noise, rotation, scaling and shearing high classification results have been optained.
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