File Download
Supplementary

Conference Paper: Wavelet-based Detection of Local Textile Defects

TitleWavelet-based Detection of Local Textile Defects
Authors
Pang, GKHKumar, A
KeywordsQuality Assurance
Defect Detection
Computer Vision
Wavelet Analysis
Industrial Automation
Issue Date2001
PublisherIEEE.
Citation
The 8th IEEE Conference on Mechatronics and Machine Vision in Practice Proceedings, Hong Kong, China, 27-29 August 2001, p. 428-431 How to Cite?
AbstractIn this paper, the problem of fabric defect detection using machine vision is investigated. Every inspection image is used to generate two projection signals along the horizontal and vertical planes respectively. Each of these signals is then normalized to have zero mean and unity variance. Wavelet decomposition of these normalized projection signals is used to enhance defect information. A simple thresholding operation on these wavelet coefficients extracts the defects and their localization. Experimental results presented in this paper show that this approach is highly successful in detecting variety of fabric defects and, can provide low-cost, single PC-based, solution to the web inspection problem.
Persistent Identifierhttp://hdl.handle.net/10722/48459
ISBN
9624421919

 

DC FieldValueLanguage
dc.contributor.authorPang, GKHen_HK
dc.contributor.authorKumar, Aen_HK
dc.date.accessioned2008-05-22T04:13:41Z-
dc.date.available2008-05-22T04:13:41Z-
dc.date.issued2001en_HK
dc.identifier.citationThe 8th IEEE Conference on Mechatronics and Machine Vision in Practice Proceedings, Hong Kong, China, 27-29 August 2001, p. 428-431en_HK
dc.identifier.isbn9624421919en_HK
dc.identifier.urihttp://hdl.handle.net/10722/48459-
dc.description.abstractIn this paper, the problem of fabric defect detection using machine vision is investigated. Every inspection image is used to generate two projection signals along the horizontal and vertical planes respectively. Each of these signals is then normalized to have zero mean and unity variance. Wavelet decomposition of these normalized projection signals is used to enhance defect information. A simple thresholding operation on these wavelet coefficients extracts the defects and their localization. Experimental results presented in this paper show that this approach is highly successful in detecting variety of fabric defects and, can provide low-cost, single PC-based, solution to the web inspection problem.en_HK
dc.format.extent548366 bytes-
dc.format.extent4651 bytes-
dc.format.extent4353 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 Proceedings-
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.subjectQuality Assuranceen_HK
dc.subjectDefect Detectionen_HK
dc.subjectComputer Visionen_HK
dc.subjectWavelet Analysisen_HK
dc.subjectIndustrial Automationen_HK
dc.titleWavelet-based Detection of Local Textile Defectsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=9624421919&volume=&spage=428&epage=431&date=2001&atitle=Wavelet-based+Detection+of+Local+Textile+Defectsen_HK
dc.identifier.emailPang, GKH: gpang@eee.hku.hken_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.hkuros73380-
dc.identifier.spage428-
dc.identifier.epage431-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats