3 October 1995 Reliable flaw classifiers for machine vision-based quality control
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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.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Johannes P.F. D'Haeyer, Johannes P.F. D'Haeyer, } "Reliable flaw classifiers for machine vision-based quality control", Proc. SPIE 2597, Machine Vision Applications, Architectures, and Systems Integration IV, (3 October 1995); doi: 10.1117/12.223971; https://doi.org/10.1117/12.223971


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