In M. Pietikäinen (Ed.), Texture Analysis in Machine Vision, Series in Machine Perception and Artificial Intelligence - vol. 40, World Scientific, pages 231-238, 2000.

Abstract

In this paper a simple and fast approach to unsupervised segmentation of surface defects is described. A set of simple texture measures (the local binary pattern method) and the statistical self-organizing map are used to detect defects in surface images. Defect detection can be seen as a typical two-class segmentation problem where the desired classes are good surface and defected surface. The main idea in this paper is to train a two-class classifier only with fault-free surface samples which is an unexpected approach. The proposed defect detection is based on the following idea: an unknown sample is classified as a defect if it differs enough from the estimated distribution of fault-free samples. The statistical self-organizing map is used to estimate this distribution. Surface images are used to demonstrate the proposed procedure.