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Proceedings Paper

Reliable flaw classifiers for machine vision-based quality control
Author(s): Johannes P.F. D'Haeyer
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Paper Abstract

The paper presents a hybrid approach to the problem of classifier construction for machine vision based inspection systems. The method allows the user to integrate different types of classifiers and exploit different sources of information such as sample data and expert knowledge. Of particular interest is the problem of classification reliability in the case of small training sets. A novel forward fuzzy decision tree induction method is proposed to handle different types of uncertainty. The performance of the method is compared experimentally with other classifiers using artificial and machine vision data.

Paper Details

Date Published: 3 October 1995
PDF: 12 pages
Proc. SPIE 2597, Machine Vision Applications, Architectures, and Systems Integration IV, (3 October 1995); doi: 10.1117/12.223971
Show Author Affiliations
Johannes P.F. D'Haeyer, Univ. of Ghent and Babbage Institute for Knowledge and Information Technology (Belgium)


Published in SPIE Proceedings Vol. 2597:
Machine Vision Applications, Architectures, and Systems Integration IV
Bruce G. Batchelor; Susan Snell Solomon; Frederick M. Waltz, Editor(s)

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