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Proceedings Paper

Distortion-Invariant Associative Memories And Processors
Author(s): David Casasent; Brian Telfer
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Paper Abstract

Associative memories represent a major new artificial intelligence type of processor. We consider their use in pattern recognition, with particular attention to distortion-invariant and adaptive pattern recognition. New associative memory techniques and pattern recognition oriented architectures suitable for multi-class distortion-invariant pattern recognition (including systems that provide adaptive updating, forgetting, achieve reduced dynamic range and improved performance) are discussed and initial results presented. The first results of distortion-invariance, multi-class associative memories for pattern recognition are presented together with new architectures and algorithms for multi-stage associative processors, iterative processors for associative memory synthesis, and multi-class distortion-invariant associative processors. The issue of orthogonal projection vectors, associative memory capacity and new results and techniques to synthesize associative memories are included throughout.

Paper Details

Date Published: 10 December 1986
PDF: 18 pages
Proc. SPIE 0697, Applications of Digital Image Processing IX, (10 December 1986); doi: 10.1117/12.976205
Show Author Affiliations
David Casasent, Carnegie Mellon University (United States)
Brian Telfer, Carnegie Mellon University (United States)

Published in SPIE Proceedings Vol. 0697:
Applications of Digital Image Processing IX
Andrew G. Tescher, Editor(s)

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