Technical Report A34, Helsinki University of Technology, Laboratory of Computer and Information Science, March 1996.

Abstract

A segmentation scheme to detect surface defects is proposed. An unsupervised neural network, the Self-Organizing Map, is used to estimate the distribution of faulty-free samples. An unknown sample is classified as a defect if it differs enough from this estimated distribution. A new scheme for determining this difference is suggested. The scheme makes use of the Voronoi set of each map unit and defines a new rule for finding the best-matching map unit. The proposed scheme is general in the sense that it can be applied to fault detection of different types of surfaces. However, it may be necessary to reselect features to take into account the specific properties of the surface type.