1 November 1991 Localized feature selection to maximize discrimination
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Abstract
We present an automatic method of designing correlation filters for pattern recognition that are composed of select local features (i.e., small parts of a reference object). The local features are selected for their ability to discriminate between the reference object and other known objects or patterns. In the basic localized feature selection problem, we design a correlation filter from a single optimal local feature. In the general localized feature selection problem, we design a correlation filter composed of several local features. We show that the discrimination ability of a correlation filter designed form properly selected local features is actually greater than the discrimination ability of a traditional matched filter.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kenneth A. Duell, Mark O. Freeman, "Localized feature selection to maximize discrimination", Proc. SPIE 1564, Optical Information Processing Systems and Architectures III, (1 November 1991); doi: 10.1117/12.49693; https://doi.org/10.1117/12.49693
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