Share Email Print

Proceedings Paper

Learned Pattern Recognition Using Synthetic-Discriminant-Functions
Author(s): David A. Jared; David J. Ennis
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 15 October 1986
PDF: 11 pages
Proc. SPIE 0638, Hybrid Image Processing, (15 October 1986); doi: 10.1117/12.964268
Show Author Affiliations
David A. Jared, Sterling Software (United States)
David J. Ennis, NASA Ames Research Center (United States)

Published in SPIE Proceedings Vol. 0638:
Hybrid Image Processing
David P. Casasent; Andrew G. Tescher, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?