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Conference Paper: Identification of surface defects in textured materials using wavelet packets

TitleIdentification of surface defects in textured materials using wavelet packets
Authors
KeywordsEngineering
Electrical engineering
Issue Date2001
PublisherIEEE.
Citation
Conference Record - Ias Annual Meeting (Ieee Industry Applications Society), 2001, v. 1, p. 247-251 How to Cite?
AbstractThis paper investigates a new approach for the detection of surface defects, in textured materials, using wavelet packets. Every inspection image is decomposed with a family of real orthonormal wavelet bases. The wavelet packet coefficients from a set of dominant frequency channels containing significant information are used for the characterization of textured images. A fixed number of shift invariant measures from the wavelet packet coefficients are computed. The magnitude and position of these shift invariant measures in a quadtree representation forms the feature set for a two-layer neural network classifier. The neural net classifier classifies these feature vectors into either of defect or defect-free classes. The experimental results suggest that this proposed scheme can successfully identify the defects, and can be used for automated visual inspection.
Persistent Identifierhttp://hdl.handle.net/10722/46325
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorKumar, Aen_HK
dc.contributor.authorPang, Gen_HK
dc.date.accessioned2007-10-30T06:47:23Z-
dc.date.available2007-10-30T06:47:23Z-
dc.date.issued2001en_HK
dc.identifier.citationConference Record - Ias Annual Meeting (Ieee Industry Applications Society), 2001, v. 1, p. 247-251en_HK
dc.identifier.issn0197-2618en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46325-
dc.description.abstractThis paper investigates a new approach for the detection of surface defects, in textured materials, using wavelet packets. Every inspection image is decomposed with a family of real orthonormal wavelet bases. The wavelet packet coefficients from a set of dominant frequency channels containing significant information are used for the characterization of textured images. A fixed number of shift invariant measures from the wavelet packet coefficients are computed. The magnitude and position of these shift invariant measures in a quadtree representation forms the feature set for a two-layer neural network classifier. The neural net classifier classifies these feature vectors into either of defect or defect-free classes. The experimental results suggest that this proposed scheme can successfully identify the defects, and can be used for automated visual inspection.en_HK
dc.format.extent1537083 bytes-
dc.format.extent4651 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofConference Record - IAS Annual Meeting (IEEE Industry Applications Society)en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2001 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.subjectEngineeringen_HK
dc.subjectElectrical engineeringen_HK
dc.titleIdentification of surface defects in textured materials using wavelet packetsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0197-2618&volume=1&spage=247&epage=251&date=2001&atitle=Identification+of+surface+defects+in+textured+materials+using+wavelet+packetsen_HK
dc.identifier.emailPang, G:gpang@eee.hku.hken_HK
dc.identifier.authorityPang, G=rp00162en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/IAS.2001.955418en_HK
dc.identifier.scopuseid_2-s2.0-0035152067en_HK
dc.identifier.hkuros72436-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035152067&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume1en_HK
dc.identifier.spage247en_HK
dc.identifier.epage251en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridKumar, A=35198218300en_HK
dc.identifier.scopusauthoridPang, G=7103393283en_HK

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