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15 October 1986 Learned Pattern Recognition Using Synthetic-Discriminant-Functions
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Proceedings Volume 0638, Hybrid Image Processing; (1986) https://doi.org/10.1117/12.964268
Event: 1986 Technical Symposium Southeast, 1986, Orlando, United States
Abstract
A method of using synthetic-discriminant-functions to facilitate learning in a pattern recognition system is discussed. Learning is accomplished by continually adding images to the training set used for synthetic discriminant functions (SDF) construction. Object identification is performed by efficiently searching a library of SDF filters for the maximum optical correlation. Two library structures are discussed--binary tree and multilinked graph--along with maximum ascent, back-tracking, perturbation, and simulated annealing searching techniques. By incorporating the distortion invariant properties of SDFs within a library structure, a robust pattern recognition system can be produced.
© (1986) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David A. Jared and David J. Ennis "Learned Pattern Recognition Using Synthetic-Discriminant-Functions", Proc. SPIE 0638, Hybrid Image Processing, (15 October 1986); https://doi.org/10.1117/12.964268
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