Unsupervised segmentation of surface defects

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 fault-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.