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Proceedings Paper

Unsupervised segmentation of defect images
Author(s): Jukka Iivarinen
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Paper Abstract

In industrial inspection one of the key areas is detection of defects from textured surfaces. The goal is to differentiate between a good, normal surface texture and a defected surface texture. In this paper this is achieved with a two-class classifier that is taught only with fault-free samples of surface texture. An unsupervised segmentation scheme is formulated where an unknown sample is classified as a defect if it differs enough from the estimated distribution of texture features extracted from fault-free samples. The extension of the self-organizing map (SOM) algorithm, the so-called statistical SOM, is used to estimate the distribution. Different versions of the statistical SOM are introduced and their computational requirements are discussed. The proposed methods are shown to perform well in segmentation of texture surface images with different kinds of defects.

Paper Details

Date Published: 5 October 2001
PDF: 8 pages
Proc. SPIE 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, (5 October 2001); doi: 10.1117/12.444218
Show Author Affiliations
Jukka Iivarinen, Helsinki Univ. of Technology (Finland)

Published in SPIE Proceedings Vol. 4572:
Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall, Editor(s)

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