Learning method for the inspection of continuously repeated patterns
| Autor: | Bruce G. Batchelor, John Paul Chan |
|---|---|
| Rok vydání: | 1992 |
| Předmět: |
Class (computer programming)
Artificial neural network Machine vision business.industry Computer science Feature extraction Machine learning computer.software_genre Visual inspection Pattern recognition (psychology) Factory (object-oriented programming) Unsupervised learning Artificial intelligence business computer |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.132093 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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