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Proceedings Paper

Real-Time 3-D Object Classification Using a Learning System
Author(s): Raymond Rimey; Philip Gouin; Christopher Scofield; Douglas L. Reilly
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Paper Abstract

We describe some experiments in real-time 3-D object classification using a learning system derived from a general neural model for supervised learning. The primary advantages of the learning system are its ability to learn from experience to recognize patterns and its inherent massive parallelism. Our motivation is to examine the feasibility and merits of the learning system in a simple machine vision problem.

Paper Details

Date Published: 27 March 1987
PDF: 6 pages
Proc. SPIE 0726, Intelligent Robots and Computer Vision V, (27 March 1987); doi: 10.1117/12.937773
Show Author Affiliations
Raymond Rimey, Nestor, Inc. (United States)
Philip Gouin, Nestor, Inc. (United States)
Christopher Scofield, Nestor, Inc. (United States)
Douglas L. Reilly, Nestor, Inc. (United States)

Published in SPIE Proceedings Vol. 0726:
Intelligent Robots and Computer Vision V
David P. Casasent, Editor(s)

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