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Proceedings Paper

Learning method for the inspection of continuously repeated patterns
Author(s): John Paul Chan; Bruce G. Batchelor
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Paper Abstract

There are many products that are produced as a continuous ribbon, and contain repeated patterns or features. There is a need for unsupervised learning of these products so that automated inspection can be performed. With many inspection tasks however, the problem is not deciding what class of product is being examined, but to distinguish a good product from a bad product. With established classification methods, it would be necessary to present a representative sample of all `bad' products to the system for training, as well as a `good' class. It is highly improbable that this could be achieved within the workings of a production factory. Automated inspection requires recognition techniques that train on only good samples, or one- class learning/recognition. This paper describes a machine vision method which learns from good examples shown to the system. From this, a knowledge base is created and used for the subsequent inspection of these patterns.

Paper Details

Date Published: 1 November 1992
PDF: 9 pages
Proc. SPIE 1823, Machine Vision Applications, Architectures, and Systems Integration, (1 November 1992); doi: 10.1117/12.132093
Show Author Affiliations
John Paul Chan, Univ. of Wales College Cardiff (United Kingdom)
Bruce G. Batchelor, Univ. of Wales College Cardiff (United Kingdom)


Published in SPIE Proceedings Vol. 1823:
Machine Vision Applications, Architectures, and Systems Integration
Bruce G. Batchelor; Susan Snell Solomon; Frederick M. Waltz, Editor(s)

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