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Neural network image segmentation for automated visual inspection

 
: Schramm, U.; Spinnler, K.P.

Aleksander, I.; Taylor, J. ; European Neural Network Society; International Neural Network Society:
Artificial neural networks, 2. Proceedings of the 1992 International Conference on Artifical Neural Networks
Amsterdam: North-Holland, 1992
ISBN: 0-444-89488-8
pp.1509-1512
International Conference on Artificial Neural Networks (ICANN) <2, 1992, Brighton>
English
Conference Paper
Fraunhofer IIS A ( IIS) ()
Bildverarbeitung; image processing; neural net; neuronales Netzwerk; Oberflächenprüfung; Qualitätskontrolle; quality control; Sichtprüfung; surface inspection; visual inspection

Abstract
A fundamental problem, not satisfactory solved for automated visual inspection, is the segmentaiton of images. To overcome the segmentation problem we suggest a fast and intelligent segmentation for advanced vision systems. The main idea of our approach is to combine the power of textgure segmentaiotn with the ability to learn of neural networks. In this paper we focus on the evaluation of different neural netowrk model.s Two kinds of feed-forward networks are compared: The multi-layered perceptron MLP and the restricted coloumb energy model (RCE). To evaluate the performance for industrial applications we calculate not only the rate of correct/incorrect classifications but we also take into consideration the computatiuonal effort and the possibility of retraining and rejection.

: http://publica.fraunhofer.de/documents/PX-26018.html