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27 March 1995 Neural network for defect classification in industrial inspection
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Proceedings Volume 2423, Machine Vision Applications in Industrial Inspection III; (1995)
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1995, San Jose, CA, United States
In this paper a neural network to perform surface defects detection in process control and automated inspection is described. Since a lot ofmaterials and defects to be inspected are rich in textural information, we propose to perform automated inspection using a texture classifier. In particular, in this work, we will ' deal with oriented textures, that are characterized by a dominant local orientation everywhere, varying locally and without a primitive element. We analyze an oriented texture image by representing it in a vector space: to each point (,y)is associated a 2D vector with the direction recovered from the dominant local orientation and the module proportional to the coherence (the degree of anisotropy of the vector image). The vector field is analyzed by projecting it on a set of linearly dependent vectors, which may or may not completely span the vector space: we find the optimal projections onto each one by satisfying global optimization criteria using a least-square-error technique implemented on an adaptive neural network. The coefficients of the projection in the basis vector are the texture parameters by means of which the texture classification is performed. Keywords: industrial inspection, oriented texture analysis, orientation field, vector space, parameter space, neural network.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Antonella Branca, Oronzo Quarta, William Delaney, and Arcangelo Distante "Neural network for defect classification in industrial inspection", Proc. SPIE 2423, Machine Vision Applications in Industrial Inspection III, (27 March 1995);

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