Learning method for the inspection of continuously repeated patterns

John Paul Chan; Bruce G. Batchelor

doi:10.1117/12.132093

1 November 1992 Learning method for the inspection of continuously repeated patterns

John Paul Chan, Bruce G. Batchelor

Proceedings Volume 1823, Machine Vision Applications, Architectures, and Systems Integration; (1992) https://doi.org/10.1117/12.132093
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States

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.

Citation Download Citation

John Paul Chan and Bruce G. Batchelor "Learning method for the inspection of continuously repeated patterns", Proc. SPIE 1823, Machine Vision Applications, Architectures, and Systems Integration, (1 November 1992); https://doi.org/10.1117/12.132093

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