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Proceedings Paper

Optical Associative Processors For Visual Perception"
Author(s): David Casasent; Brian Telfer
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Paper Abstract

We consider various associative processor modifications required to allow these systems to be used for visual perception, scene analysis, and object recognition. For these applications, decisions on the class of the objects present in the input image are required and thus heteroassociative memories are necessary (rather than the autoassociative memories that have been given most attention). We analyze the performance of both associative processors and note that there is considerable difference between heteroassociative and autoassociative memories. We describe associative processors suitable for realizing functions such as: distortion invariance (using linear discriminant function memory synthesis techniques), noise and image processing performance (using autoassociative memories in cascade with with a heteroassociative processor and with a finite number of autoassociative memory iterations employed), shift invariance (achieved through the use of associative processors operating on feature space data), and the analysis of multiple objects in high noise (which is achieved using associative processing of the output from symbolic correlators). We detail and provide initial demonstrations of the use of associative processors operating on iconic, feature space and symbolic data, as well as adaptive associative processors.

Paper Details

Date Published: 3 May 1988
PDF: 15 pages
Proc. SPIE 0882, Neural Network Models for Optical Computing, (3 May 1988); doi: 10.1117/12.944100
Show Author Affiliations
David Casasent, Carnegie Mellon University (United States)
Brian Telfer, Carnegie Mellon University (United States)


Published in SPIE Proceedings Vol. 0882:
Neural Network Models for Optical Computing
Ravindra A. Athale; Joel Davis, Editor(s)

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