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Proceedings Paper

Surface defect detection with histogram-based texture features
Author(s): Jukka Iivarinen
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Paper Abstract

In this paper the performance of two histogram-based texture analysis techniques for surface defect detection is evaluated. These techniques are the co-occurrence matrix method and the local binary pattern method. Both methods yield a set of texture features that are computed form a small image window. The unsupervised segmentation procedure is used in the experiments. It is based on the statistical self-organizing map algorithm that is trained only with fault-free surface samples. Results of experiments with both feature sets are good and there is no clear difference in their performances. The differences are found in their computational requirements where the features of the local binary pattern method are better in several aspects.

Paper Details

Date Published: 11 October 2000
PDF: 6 pages
Proc. SPIE 4197, Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision, (11 October 2000); doi: 10.1117/12.403757
Show Author Affiliations
Jukka Iivarinen, Helsinki Univ. of Technology (Finland)


Published in SPIE Proceedings Vol. 4197:
Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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