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ABSTRACT
Visual quality control is a demanding task of increasing importance in industrial manufacturing. Both speed and flexibility are of paramount importance for viable and competitive inspection systems. In our work we developed a dedicated neural network architecture for anomaly detection that can easily be trained by a single presentation of examples and is amenable to massively parallel VLSI implementation. Our focus is on our ASIC and prototype system design effort for this network. Keywords: Neural networks, VLSI, novelty filter, anomaly detection, automated visual industrial quality control.
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CITATION
Peter Windirsch, Andreas König, Michael Gasteier, Manfred Glesner, "Visual Inspection in Industrial Manufacturing", IEEE Micro, vol. 15, no. , pp. 26-31, June 1995, doi:10.1109/40.387679
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