Adaptive Learning Optical Symbolic Processor

David Casasent; Abhijit Mahalanobis

doi:10.1117/12.944098

3 May 1988 Adaptive Learning Optical Symbolic Processor

David Casasent, Abhijit Mahalanobis

Proceedings Volume 0882, Neural Network Models for Optical Computing; (1988) https://doi.org/10.1117/12.944098
Event: 1988 Los Angeles Symposium: O-E/LASE '88, 1988, Los Angeles, CA, United States

Abstract

Partitioning of an object into N parts and the use of M filters with different output patterns are used to produce an NM digit symbolic encoding of the input object. The rule based system and techniques to update partitions of the object are emphasized in this paper. Three-dimensional aspect-invariant, shift-invariant and distortion-invariant pattern recognition data are considered and provided to demonstrate the usefulness of this technique for adaptive image processing.

Citation Download Citation

David Casasent and Abhijit Mahalanobis "Adaptive Learning Optical Symbolic Processor", Proc. SPIE 0882, Neural Network Models for Optical Computing, (3 May 1988); https://doi.org/10.1117/12.944098

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