Proceedings of the 6th International Conference on Information Engineering for Mechanics and Materials

Fabric Defect Detection based on GLCM Approach

Authors
Xiaowei Zhang, Xiujuan Fan
Corresponding Author
Xiaowei Zhang
Available Online November 2016.
DOI
https://doi.org/10.2991/icimm-16.2016.120How to use a DOI?
Keywords
GLCM; Fabric defect detection; texture features
Abstract

In general, an image of woven fabric sample can be regarded as a typical textured image. The detection of local fabric defects is one of the most captivating problems in computer vision and has received much attention over the years. In the textile industry, careful inspections for woven fabrics have to be carried out because fabric defects may reduce the profit of a company by 45% or 65%. Real time automated fabric defect detection plays a crucial role in the textile manufacturing industry to ensure that the industry meets its high quality standards. Indeed. The production of good quality products is a key issue for increasing profitability and customer satisfaction and thus improving the industry's competitive edge in the global market. If defects in the fabrics are not discovered prior to the garment manufacturing process, significant financial losses can incur.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 6th International Conference on Information Engineering for Mechanics and Materials
Series
Advances in Engineering Research
Publication Date
November 2016
ISBN
978-94-6252-244-2
ISSN
2352-5401
DOI
https://doi.org/10.2991/icimm-16.2016.120How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xiaowei Zhang
AU  - Xiujuan Fan
PY  - 2016/11
DA  - 2016/11
TI  - Fabric Defect Detection based on GLCM Approach
BT  - Proceedings of the 6th International Conference on Information Engineering for Mechanics and Materials
PB  - Atlantis Press
SP  - 673
EP  - 677
SN  - 2352-5401
UR  - https://doi.org/10.2991/icimm-16.2016.120
DO  - https://doi.org/10.2991/icimm-16.2016.120
ID  - Zhang2016/11
ER  -