Jukka Iivarinen(1), Juhani Rauhamaa(2), and Ari Visa(1)
(1)Helsinki University of Technology
Laboratory of Computer and Information Science
Rakentajanaukio 2 C, FIN-02150 Espoo, Finland
(2)ABB Industry Oy
Pulp & Paper Division
P.O. Box 94, FIN-00381 Helsinki, Finland
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.