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Proceedings Paper

A self-learning machine vision system
Author(s): Michael Kelley
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Paper Abstract

Reliable and productive manufacturing operations have depended on people to quickly detect and solve problems whenever they appear. Over the last 20 years, more and more manufacturing operations have embraced machine vision systems to increase productivity, reliability and cost-effectiveness, including reducing the number of human operators required. Because of these two key factors, increased technical complexity and an fewer resources, the people who continue to work in the factory are finding it ever more difficult to deal with issues that involve the production line's sophisticated machine vision equipment. An image processing technology is now available that enables a system to match an operator’s subjectivity. A hardware-based implementation of a neural network system enables a vision system to "think" and "inspect" like a human, with the speed and reliability of a machine.

Paper Details

Date Published: 4 March 2004
PDF: 11 pages
Proc. SPIE 5263, Intelligent Manufacturing, (4 March 2004); doi: 10.1117/12.518547
Show Author Affiliations
Michael Kelley, JAI PULNiX Inc. (United States)

Published in SPIE Proceedings Vol. 5263:
Intelligent Manufacturing
Bhaskaran Gopalakrishnan; Peter E. Orban; Angappa Gunasekaran, Editor(s)

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