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Article: Wavelet based methods on patterned fabric defect detection

TitleWavelet based methods on patterned fabric defect detection
Authors
Ngan, HYTPang, GKHYung, SPNg, MK
KeywordsDefect detection
Patterned fabric inspection
Patterned texture
Texture analysis
Wavelet transform
Issue Date2005
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pr
Citation
Pattern Recognition, 2005, v. 38 n. 4, p. 559-576 How to Cite?
DOI: http://dx.doi.org/10.1016/j.patcog.2004.07.009
AbstractThe wavelet transform (WT) has been developed over 20 years and successfully applied in defect detection on plain (unpatterned) fabric. This paper is on the use of the wavelet transform to develop an automated visual inspection method for defect detection on patterned fabric. A method called direct thresholding (DT) based on WT detailed subimages has been developed. The golden image subtraction method (GIS) is also introduced. GIS is an efficient and fast method, which can segment out the defective regions on patterned fabric effectively. In this paper, the method of wavelet preprocessed golden image subtraction (WGIS) has been developed for defect detection on patterned fabric or repetitive patterned texture. This paper also presents a comparison of the three methods. It can be concluded that the WGIS method provides the best detection result. The overall detection success rate is 96.7% with 30 defect-free images and 30 defective patterned images for one common kind of patterned Jacquard fabric. © 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/73736
ISSN
0031-3203
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 2.732
ISI Accession Number ID
WOS:000226434500010
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorNgan, HYTen_HK
dc.contributor.authorPang, GKHen_HK
dc.contributor.authorYung, SPen_HK
dc.contributor.authorNg, MKen_HK
dc.date.accessioned2010-09-06T06:54:16Z-
dc.date.available2010-09-06T06:54:16Z-
dc.date.issued2005en_HK
dc.identifier.citationPattern Recognition, 2005, v. 38 n. 4, p. 559-576en_HK
dc.identifier.issn0031-3203en_HK
dc.identifier.urihttp://hdl.handle.net/10722/73736-
dc.description.abstractThe wavelet transform (WT) has been developed over 20 years and successfully applied in defect detection on plain (unpatterned) fabric. This paper is on the use of the wavelet transform to develop an automated visual inspection method for defect detection on patterned fabric. A method called direct thresholding (DT) based on WT detailed subimages has been developed. The golden image subtraction method (GIS) is also introduced. GIS is an efficient and fast method, which can segment out the defective regions on patterned fabric effectively. In this paper, the method of wavelet preprocessed golden image subtraction (WGIS) has been developed for defect detection on patterned fabric or repetitive patterned texture. This paper also presents a comparison of the three methods. It can be concluded that the WGIS method provides the best detection result. The overall detection success rate is 96.7% with 30 defect-free images and 30 defective patterned images for one common kind of patterned Jacquard fabric. © 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pren_HK
dc.relation.ispartofPattern Recognitionen_HK
dc.subjectDefect detectionen_HK
dc.subjectPatterned fabric inspectionen_HK
dc.subjectPatterned textureen_HK
dc.subjectTexture analysisen_HK
dc.subjectWavelet transformen_HK
dc.titleWavelet based methods on patterned fabric defect detectionen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0031-3203&volume=38&issue=4&spage=559&epage=576&date=2005&atitle=Wavelet+based+methods+on+patterned+fabric+defect+detectionen_HK
dc.identifier.emailPang, GKH:gpang@eee.hku.hken_HK
dc.identifier.emailYung, SP:spyung@hkucc.hku.hken_HK
dc.identifier.authorityPang, GKH=rp00162en_HK
dc.identifier.authorityYung, SP=rp00838en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.patcog.2004.07.009en_HK
dc.identifier.scopuseid_2-s2.0-10644264358en_HK
dc.identifier.hkuros98096en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-10644264358&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume38en_HK
dc.identifier.issue4en_HK
dc.identifier.spage559en_HK
dc.identifier.epage576en_HK
dc.identifier.isiWOS:000226434500010-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridNgan, HYT=7102173824en_HK
dc.identifier.scopusauthoridPang, GKH=7103393283en_HK
dc.identifier.scopusauthoridYung, SP=7006540951en_HK
dc.identifier.scopusauthoridNg, MK=34571761900en_HK
dc.identifier.citeulike6043850-
dc.identifier.issnl0031-3203-

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