In M. Pietikäinen and H. Kauppinen (Eds.), Workshop on Texture Analysis in Machine Vision, pages 53-58, Oulu, Finland, June 14-15 1999.

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.