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Directional Defects in Fabrics

Directional Defects in Fabrics This paper deals with a procedure that recognizes common defects occurring in woven fabric. Images of woven fabric are considered as having a directional texture due to their periodical nature. We used a statistical approach based on the analysis of periodicity of texture images in horizontal and vertical directions. These periodicities correspond to the periodicity of second-order grey level statistical features obtained from a grey level co-occurrence matrix. A set of five significant features is extracted from the matrix: energy, correlation, homogeneity, cluster shade and cluster prominence. The presence of a defect over texture causes regular structure changes and consequently, statistical changes. Detection algorithm is based on the sliding window technique; the window is moved over the whole image area. We counted the test statistic for every window and the multivariate control charts are used as a tool for judging the existence of defects. The results show that the statistical approach is suitable for detection of directional defects or changes in regular structure in analysed simulated and real fabrics. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research Journal of Textile and Apparel Emerald Publishing

Directional Defects in Fabrics

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References (8)

Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1560-6074
DOI
10.1108/RJTA-12-02-2008-B002
Publisher site
See Article on Publisher Site

Abstract

This paper deals with a procedure that recognizes common defects occurring in woven fabric. Images of woven fabric are considered as having a directional texture due to their periodical nature. We used a statistical approach based on the analysis of periodicity of texture images in horizontal and vertical directions. These periodicities correspond to the periodicity of second-order grey level statistical features obtained from a grey level co-occurrence matrix. A set of five significant features is extracted from the matrix: energy, correlation, homogeneity, cluster shade and cluster prominence. The presence of a defect over texture causes regular structure changes and consequently, statistical changes. Detection algorithm is based on the sliding window technique; the window is moved over the whole image area. We counted the test statistic for every window and the multivariate control charts are used as a tool for judging the existence of defects. The results show that the statistical approach is suitable for detection of directional defects or changes in regular structure in analysed simulated and real fabrics.

Journal

Research Journal of Textile and Apparel – Emerald Publishing

Published: May 1, 2008

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