This paper discusses the application of a back-propagation multi-layer perceptron and a learning vector quantization network to the classification of defects in valve stem seals for car engines.

    Both networks were trained with vectors containing descriptive attributes of known flaws. These attribute vectors (‘signatures’) were extracted from images of the seals captured by an industrial vision system. The paper describes the hardware and techniques used and the results obtained.


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    Title :

    Neural Classifiers for Automated Visual Inspection


    Contributors:


    Publication date :

    1994-04-01


    Size :

    7 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Neural classifiers for automated visual inspection

    Pham,D.T. / Byro-Corrochano,E.J. / Univ.of Wales,College of Cardiff,School of Electrical, Electronic and Systems Engng.,GB | Automotive engineering | 1994


    Neural classifiers for automated visual inspection

    Pham, D.T. | Online Contents | 1994


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