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Conference Paper: Fabric defect classification using wavelet frames and minimum classification error training

TitleFabric defect classification using wavelet frames and minimum classification error training
Authors
KeywordsDefect classification
Fabric inspection
Minimum classification error
Wavelet frames
Issue Date2002
PublisherIEEE.
Citation
Conference Record - Ias Annual Meeting (Ieee Industry Applications Society), 2002, v. 1, p. 290-296 How to Cite?
AbstractThis paper proposes a new method for fabric defect classification by incorporating the design of a wavelet frames based feature extractor with the design of an Euclidean distance based classifier. Channel variances at the outputs of the wavelet frame decomposition are used to characterize each nonoverlapping window of the fabric image. A feature extractor using linear transformation matrix is further employed to extract the classification-oriented features. With an Euclidean distance based classifier, each nonoverlapping window of the fabric image is then assigned to its corresponding category. Minimization of the classification error is achieved by incorporating the design of the feature extractor with the design of the classifier based on Minimum Classification Error (MCE) training method. The proposed method has been evaluated on the classification of 329 defect samples containing nine classes of fabric defects, and 328 nondefect samples, where 93.1% classification accuracy has been achieved.
Persistent Identifierhttp://hdl.handle.net/10722/54049
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorYang, Xen_HK
dc.contributor.authorPang, Gen_HK
dc.contributor.authorYung, Nen_HK
dc.date.accessioned2009-04-03T07:35:26Z-
dc.date.available2009-04-03T07:35:26Z-
dc.date.issued2002en_HK
dc.identifier.citationConference Record - Ias Annual Meeting (Ieee Industry Applications Society), 2002, v. 1, p. 290-296en_HK
dc.identifier.issn0197-2618en_HK
dc.identifier.urihttp://hdl.handle.net/10722/54049-
dc.description.abstractThis paper proposes a new method for fabric defect classification by incorporating the design of a wavelet frames based feature extractor with the design of an Euclidean distance based classifier. Channel variances at the outputs of the wavelet frame decomposition are used to characterize each nonoverlapping window of the fabric image. A feature extractor using linear transformation matrix is further employed to extract the classification-oriented features. With an Euclidean distance based classifier, each nonoverlapping window of the fabric image is then assigned to its corresponding category. Minimization of the classification error is achieved by incorporating the design of the feature extractor with the design of the classifier based on Minimum Classification Error (MCE) training method. The proposed method has been evaluated on the classification of 329 defect samples containing nine classes of fabric defects, and 328 nondefect samples, where 93.1% classification accuracy has been achieved.en_HK
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofConference Record - IAS Annual Meeting (IEEE Industry Applications Society)en_HK
dc.rights©2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectDefect classificationen_HK
dc.subjectFabric inspectionen_HK
dc.subjectMinimum classification erroren_HK
dc.subjectWavelet framesen_HK
dc.titleFabric defect classification using wavelet frames and minimum classification error trainingen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0197-2618&volume=1&spage=290&epage=296&date=2002&atitle=Fabric+defect+classification+using+wavelet+frames+and+minimum+classification+error+trainingen_HK
dc.identifier.emailPang, G:gpang@eee.hku.hken_HK
dc.identifier.emailYung, N:nyung@eee.hku.hken_HK
dc.identifier.authorityPang, G=rp00162en_HK
dc.identifier.authorityYung, N=rp00226en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.scopuseid_2-s2.0-0036444444en_HK
dc.identifier.hkuros81219-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0036444444&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume1en_HK
dc.identifier.spage290en_HK
dc.identifier.epage296en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridYang, X=7406505132en_HK
dc.identifier.scopusauthoridPang, G=7103393283en_HK
dc.identifier.scopusauthoridYung, N=7003473369en_HK

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