11 March 1994 Inspection of ceramic tableware for quality control using a neural network vision system
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Abstract
Automated ceramic tableware inspection displays many of the problems associated with automated inspection. A large number of possible faults must be considered, further complicated by the large variety of patterns with which the tableware is decorated. A hierarchical approach is taken in this work involving several levels of feature extraction and classification. Faults are detected, evaluated and the item as a whole is classified as a function of the combined fault measurements. Faults can be categorized as visual, surface, and structural. This paper concentrates on one visual fault known as shading variation, a result of color pigments registering as light, dark, or irregular. This fault is evaluated by analysis of the image intensity histogram. The histogram displays characteristics which vary with shading. Deviations from the standard are evaluated and the results passed onto a further classification stage which takes results from other feature extraction stages to give an overall evaluation of the item under inspection.
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Graham B. Finney, Graham B. Finney, J. B. Gomm, J. B. Gomm, D. Williams, D. Williams, John T. Atkinson, John T. Atkinson, } "Inspection of ceramic tableware for quality control using a neural network vision system", Proc. SPIE 2183, Machine Vision Applications in Industrial Inspection II, (11 March 1994); doi: 10.1117/12.171204; https://doi.org/10.1117/12.171204
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