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Unsupervised segmentation of surface defects

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3 Author(s)
Iivarinen, J. ; Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol., Espoo, Finland ; Rauhamaa, J. ; Visa, A.

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

Published in:

Pattern Recognition, 1996., Proceedings of the 13th International Conference on  (Volume:4 )

Date of Conference:

25-29 Aug 1996

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