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Proceedings Paper

Three-dimensional pattern recognition using an optoelectronic inner product complex neural network
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Paper Abstract

A complex associative memory model based on a neural network architecture is proposed for recognizing three-dimensional objects acquired from a dynamic environment. The storage representation of the complex associative memory model is based on an efficient amplitude modulated phase-only matched filter. The input to the memory is derived from the discrete Fourier transform of the edge coordinates of the to-be-recognized moving object, where the edges are obtained through motion-based segmentation of the image scene. An adaptive threshold is used during the decision making process to indicate a match or identify a mismatch. Computer simulation on real world data proves the effectiveness of the proposed model. The proposed scheme is readily amenable to opto-electronic implementation.

Paper Details

Date Published: 25 October 1993
PDF: 10 pages
Proc. SPIE 1959, Optical Pattern Recognition IV, (25 October 1993); doi: 10.1117/12.160315
Show Author Affiliations
Abdul Ahad Sami Awwal, Wright State Univ. (United States)
Gregory J. Power, Air Force Wright Lab. (United States)

Published in SPIE Proceedings Vol. 1959:
Optical Pattern Recognition IV
David P. Casasent, Editor(s)

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