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Conference Paper: Fabric defect classification using wavelet frames and minimum classification error-based neural network

TitleFabric defect classification using wavelet frames and minimum classification error-based neural network
Authors
KeywordsFabric inspection
defect classification
wavelet frames
neural network
minimum classification error
Issue Date2002
PublisherIEEE.
Citation
The 9th IEEE Conference on Mechatronics and Machine Vision in Practice, Chiang Mai, Thailand, 10-12 September 2002, p. 77-85 How to Cite?
AbstractThis paper presents a new method for fabric defect classification by using a wavelet frames feature extractor and a minimum classification error-based neural network. Channel variances at the outputs of the wavelet frame decomposition are extracted to characterize each non-overlapping window of the fabric image, which is further assigned to a defect category with a neural network classifier. In our work, a Minimum Classification Error (MCE) criterion is used in the training of the neural network for the improvement of classification performance. The developed defect classification method has been evaluated on the classification of 329 defect samples from nine types of defects and 82 non-defect samples, where an 93.4% classification accuracy was achieved.
Persistent Identifierhttp://hdl.handle.net/10722/48462

 

DC FieldValueLanguage
dc.contributor.authorPang, GKHen_HK
dc.contributor.authorYang, Xen_HK
dc.contributor.authorYung, Nen_HK
dc.date.accessioned2008-05-22T04:13:47Z-
dc.date.available2008-05-22T04:13:47Z-
dc.date.issued2002en_HK
dc.identifier.citationThe 9th IEEE Conference on Mechatronics and Machine Vision in Practice, Chiang Mai, Thailand, 10-12 September 2002, p. 77-85en_HK
dc.identifier.urihttp://hdl.handle.net/10722/48462-
dc.description.abstractThis paper presents a new method for fabric defect classification by using a wavelet frames feature extractor and a minimum classification error-based neural network. Channel variances at the outputs of the wavelet frame decomposition are extracted to characterize each non-overlapping window of the fabric image, which is further assigned to a defect category with a neural network classifier. In our work, a Minimum Classification Error (MCE) criterion is used in the training of the neural network for the improvement of classification performance. The developed defect classification method has been evaluated on the classification of 329 defect samples from nine types of defects and 82 non-defect samples, where an 93.4% classification accuracy was achieved.en_HK
dc.format.extent2656613 bytes-
dc.format.extent4353 bytes-
dc.format.extent10863 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Conference on Mechatronics and Machine Vision in Practice-
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.subjectFabric inspectionen_HK
dc.subjectdefect classificationen_HK
dc.subjectwavelet framesen_HK
dc.subjectneural networken_HK
dc.subjectminimum classification erroren_HK
dc.titleFabric defect classification using wavelet frames and minimum classification error-based neural networken_HK
dc.typeConference_Paperen_HK
dc.identifier.emailPang, GKH: gpang@eee.hku.hken_HK
dc.identifier.emailYung, NHC: nyung@eee.hku.hken_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.hkuros82755-

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