论文信息 - A self-training visual inspection system with a neural network classifier

A self-training visual inspection system with a neural network classifier

A self-training visual inspection system using a connectionist classifier is presented. The system is composed of a control unit, a signal-processing unit, and a connectionist classifier. The control unit both generates the training set and performs the function of teacher to the classifier. The second unit compresses the two-dimensional image into a one-dimensional signal. Potential flaws extracted from the one-dimensional signal are sent to the classifier. The classifier used in this work is a standard multilayer connectionist neural network that uses backpropagation for learning. The system is applied to two inspection tasks involving two-dimensional surfaces characterized by a known intensity distribution. Diagnostics for evaluating the classifier are presented, along with an evaluation of the classifier's performance.<<ETX>>

[1] David J. Burr,et al. Experiments on neural net recognition of spoken and written text , 1988, IEEE Trans. Acoust. Speech Signal Process..

[2] A. Sideris,et al. A multilayered neural network controller , 1988, IEEE Control Systems Magazine.

[3] Terrence J. Sejnowski,et al. Analysis of hidden units in a layered network trained to classify sonar targets , 1988, Neural Networks.

[4] Charles W. Anderson,et al. Learning and problem-solving with multilayer connectionist systems (adaptive, strategy learning, neural networks, reinforcement learning) , 1986 .