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Proceedings Paper

Nonsegmenting defect detection and SOM-based classification for surface inspection using color vision
Author(s): Hannu Kauppinen; Hannu Rautio; Olli Silven
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Paper Abstract

In automated visual surface inspection based on statistical pattern recognition, the collection of training material for setting up the classifier may appear to be difficult. Getting a representative set of labeled training samples requires scanning through large amounts of image material by the training personnel, which is an error prone and laborious task. Problems are further caused by the variations of the inspected materials and imaging conditions, especially with color imaging. Approaches based on adaptive defect detection and robust features may appear inapplicable because of losing some faint or large area defects. Adjusting the classifier to adapt to the changed situation may appear difficult because of the inflexibility of the classifiers' implementations. This may lead to impractical often repeated training material collection and classifier retraining cycles. In this paper we propose a non-segmenting defect detection technique combined with a self-organizing map (SOM) based classifier and user interface. The purpose is to avoid the problems with adaptive detection techniques, and to provide an intuitive user interface for classification, helping in training material collection and labelling, and with a possibility of easily adjusting the class boundaries. The approach is illustrated with examples from wood surface inspection.

Paper Details

Date Published: 16 September 1999
PDF: 11 pages
Proc. SPIE 3826, Polarization and Color Techniques in Industrial Inspection, (16 September 1999); doi: 10.1117/12.364334
Show Author Affiliations
Hannu Kauppinen, Univ. of Oulu (Finland)
Hannu Rautio, Univ. of Oulu (Finland)
Olli Silven, Univ. of Oulu (Finland)

Published in SPIE Proceedings Vol. 3826:
Polarization and Color Techniques in Industrial Inspection
Elzbieta A. Marszalec; Emanuele Trucco, Editor(s)

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