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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.

P. Windirsch, A. König, M. Gasteier and M. Glesner, "Visual Inspection in Industrial Manufacturing," in IEEE Micro, vol. 15, no. , pp. 26-31, 1995.
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