Unsupervised segmentation of defect images

Jukka Iivarinen

doi:10.1117/12.444218

5 October 2001 Unsupervised segmentation of defect images

Jukka Iivarinen

Proceedings Volume 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision; (2001) https://doi.org/10.1117/12.444218
Event: Intelligent Systems and Advanced Manufacturing, 2001, Boston, MA, United States

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.

Citation Download Citation

Jukka Iivarinen "Unsupervised segmentation of defect images", Proc. SPIE 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, (5 October 2001); https://doi.org/10.1117/12.444218

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