Learned Pattern Recognition Using Synthetic-Discriminant-Functions

David A. Jared; David J. Ennis

doi:10.1117/12.964268

15 October 1986 Learned Pattern Recognition Using Synthetic-Discriminant-Functions

David A. Jared, David J. Ennis

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available