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Proceedings Paper

Fault detection by a Gaussian neural network with reject options in glass bottle production
Author(s): Christian Firmin; D. Hamad; Jack-Gerard Postaire; Ruo Dan Zhang
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Paper Abstract

During the production of translucent glass bottles, many inspection procedures are realized in order to eliminate defects which produce dangerous consequences for customs. Checks on the neck of a bottle, which look like cracks in the glass, are one of the most important defects. Although an automated visual inspection system has been developed to solve this specific problem, its ability to cope with variations of the environment is limited and it requires careful tuning whenever the characteristics of the production change. In this paper, we propose a new approach based on computer vision and artificial neural network for check detection. The inspection procedure involves extracting features images of necks, the selection of the most discriminant features, and the decision is realized by a Gaussian neural network with reject options.

Paper Details

Date Published: 26 August 1996
PDF: 11 pages
Proc. SPIE 2785, Vision Systems: New Image Processing Techniques, (26 August 1996); doi: 10.1117/12.248536
Show Author Affiliations
Christian Firmin, Univ. des Sciences et Technologies de Lille (France)
D. Hamad, Univ. des Sciences et Technologies de Lille (France)
Jack-Gerard Postaire, IUT (France)
Ruo Dan Zhang, Research Ctr. of B.S.N. Emballage (France)


Published in SPIE Proceedings Vol. 2785:
Vision Systems: New Image Processing Techniques
Philippe Refregier, Editor(s)

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