Learned pattern recognition using synthetic-discriminant-functionsA 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.
Document ID
19870055389
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Jared, David A. (Sterling Software Palo Alto, CA, United States)
Ennis, David J. (NASA Ames Research Center Moffett Field, CA, United States)