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24 October 1997 Pattern recognition under translation and scale changes
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Recognition of specific patterns and signatures in images has long been of interest. Powerful techniques exist for detection and classification, but are defeated by straightforward changes and variations in the pattern. These variations include translation and scale changes. Translation and scaling are well understood in a mathematical sense and transformations exist such that, when applied to an image, the result is invariant to these disturbances. Hence, methods may be designed wherein these effects are absent in the resultant representations. This paper describes a pattern recognition procedure which uses scale and translation invariant representations (STIRs) as one step of the process. A novel feature extraction method then identifies features of the STIRs orthogonal to noise variation. This is followed by a detection approach which exploits these features to detect desired patterns in noise. By explicitly modeling the variation due to noninteger scaling factors and sub-pixel translation, strong discrimination between similar patterns is achieved. Using the orthogonal features of the invariant representations, several tests are shown to classify well. A two dimensional image is the basic starting point for the technique. This may be an actual image of an object or the two dimensional form of signal representation such as a time-frequency distribution. The example of keyword spotting in scanned documents serves to illustrate the pattern recognition method.
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Eugene J. Zalubas and William J. Williams "Pattern recognition under translation and scale changes", Proc. SPIE 3162, Advanced Signal Processing: Algorithms, Architectures, and Implementations VII, (24 October 1997);

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