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Article: Fabric defect detection using morphological filters

TitleFabric defect detection using morphological filters
Authors
KeywordsDefect Detection
Gabor Wavelet Network
Morphological Filter
Quality Control
Textile Fabrics
Issue Date2009
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/imavis
Citation
Image And Vision Computing, 2009, v. 27 n. 10, p. 1585-1592 How to Cite?
AbstractIn this paper, a novel defect detection scheme based on morphological filters is proposed to tackle the problem of automated defect detection for woven fabrics. In the proposed scheme, important texture features of the textile fabric are extracted using a pre-trained Gabor wavelet network. These texture features are then used to facilitate the construction of structuring elements in subsequent morphological processing to remove the fabric background and isolate the defects. Since the proposed defect detection scheme requires a few morphological filters only, the amount of computational load involved is not significant. The performance of the proposed scheme is evaluated by using a wide variety of homogeneous textile images with different types of common fabric defects. The test results obtained exhibit accurate defect detection with low false alarms, thus showing the effectiveness and robustness of the proposed detection scheme. In addition, the proposed detection scheme is further evaluated in real time by using a prototyped automated inspection system. © 2009 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/155917
ISSN
2015 Impact Factor: 1.766
2015 SCImago Journal Rankings: 1.700
ISI Accession Number ID
Funding AgencyGrant Number
Hong Kong Special Administrative Region, ChinaHKU7382/02E
Funding Information:

The work described in this paper was supported by a grant from the research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKU7382/02E). The authors would also like to thank the reviewers for their helpful suggestions and constructive comments on the earlier versions of the paper.

References

 

DC FieldValueLanguage
dc.contributor.authorMak, KLen_US
dc.contributor.authorPeng, Pen_US
dc.contributor.authorYiu, KFCen_US
dc.date.accessioned2012-08-08T08:38:23Z-
dc.date.available2012-08-08T08:38:23Z-
dc.date.issued2009en_US
dc.identifier.citationImage And Vision Computing, 2009, v. 27 n. 10, p. 1585-1592en_US
dc.identifier.issn0262-8856en_US
dc.identifier.urihttp://hdl.handle.net/10722/155917-
dc.description.abstractIn this paper, a novel defect detection scheme based on morphological filters is proposed to tackle the problem of automated defect detection for woven fabrics. In the proposed scheme, important texture features of the textile fabric are extracted using a pre-trained Gabor wavelet network. These texture features are then used to facilitate the construction of structuring elements in subsequent morphological processing to remove the fabric background and isolate the defects. Since the proposed defect detection scheme requires a few morphological filters only, the amount of computational load involved is not significant. The performance of the proposed scheme is evaluated by using a wide variety of homogeneous textile images with different types of common fabric defects. The test results obtained exhibit accurate defect detection with low false alarms, thus showing the effectiveness and robustness of the proposed detection scheme. In addition, the proposed detection scheme is further evaluated in real time by using a prototyped automated inspection system. © 2009 Elsevier B.V. All rights reserved.en_US
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/imavisen_US
dc.relation.ispartofImage and Vision Computingen_US
dc.subjectDefect Detectionen_US
dc.subjectGabor Wavelet Networken_US
dc.subjectMorphological Filteren_US
dc.subjectQuality Controlen_US
dc.subjectTextile Fabricsen_US
dc.titleFabric defect detection using morphological filtersen_US
dc.typeArticleen_US
dc.identifier.emailMak, KL:makkl@hkucc.hku.hken_US
dc.identifier.emailYiu, KFC:cedric@hkucc.hku.hken_US
dc.identifier.authorityMak, KL=rp00154en_US
dc.identifier.authorityYiu, KFC=rp00206en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.imavis.2009.03.007en_US
dc.identifier.scopuseid_2-s2.0-67349127629en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-67349127629&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume27en_US
dc.identifier.issue10en_US
dc.identifier.spage1585en_US
dc.identifier.epage1592en_US
dc.identifier.eissn1872-8138-
dc.identifier.isiWOS:000268403800015-
dc.publisher.placeNetherlandsen_US
dc.identifier.scopusauthoridMak, KL=7102680226en_US
dc.identifier.scopusauthoridPeng, P=7102844225en_US
dc.identifier.scopusauthoridYiu, KFC=24802813000en_US
dc.identifier.citeulike4810925-

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