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Article: Fabric defect detection using multi-level tuned-matched gabor filters

TitleFabric defect detection using multi-level tuned-matched gabor filters
Authors
KeywordsDefect Detection
Industrial Inspection
Multi-Level Gabor Wavelet
Woven Fabrics
Issue Date2012
PublisherAmerican Institute of Mathematical Sciences. The Journal's web site is located at http://aimsciences.org/journals/jimo/description.htm
Citation
Journal Of Industrial And Management Optimization, 2012, v. 8 n. 2, p. 325-341 How to Cite?
AbstractThis paper proposes a new defect detection scheme for woven fab- rics. The proposed scheme is divided into two parts, namely the training part and the defect detection part. In the training part, a non-defective fabric image is used as a template image, and a finite set of multi-level Gabor wavelets are tuned to match the texture information of the image. In the defect detection part, filtered images from different levels are fused together and the constructed detection scheme is used to detect defects in fabric sample images with the same texture background as that of the template image. A filter selection method is also developed to select optimal filters to facilitate defect detection. The nov- elty of the method comes from the observation that a Gabor filter with finer resolutions than the fabric defects yarn can contribute very little for defect segmentation but need additional computational time. The proposed scheme is tested by using 78 homogeneous textile fabric images. The results exhibit ac- curate defect detections with lower false alarms than using the standard Gabor wavelets. Analysis of the computational complexity of the proposed detection scheme is derived, which shows that the scheme can be implemented in real time easily.
Persistent Identifierhttp://hdl.handle.net/10722/155964
ISSN
2015 Impact Factor: 0.776
2015 SCImago Journal Rankings: 0.639
ISI Accession Number ID
Funding AgencyGrant Number
Research Grants Council of the Hong Kong Special Administrative Region, PRCHKU 714807E
Funding Information:

The authors gratefully acknowledge the financial support from the Research Grants Council of the Hong Kong Special Administrative Region, PRC under the grant HKU 714807E for this project.

References
Grants

 

DC FieldValueLanguage
dc.contributor.authorMak, KLen_US
dc.contributor.authorPeng, Pen_US
dc.contributor.authorYiu, KFCen_US
dc.date.accessioned2012-08-08T08:38:39Z-
dc.date.available2012-08-08T08:38:39Z-
dc.date.issued2012en_US
dc.identifier.citationJournal Of Industrial And Management Optimization, 2012, v. 8 n. 2, p. 325-341en_US
dc.identifier.issn1547-5816en_US
dc.identifier.urihttp://hdl.handle.net/10722/155964-
dc.description.abstractThis paper proposes a new defect detection scheme for woven fab- rics. The proposed scheme is divided into two parts, namely the training part and the defect detection part. In the training part, a non-defective fabric image is used as a template image, and a finite set of multi-level Gabor wavelets are tuned to match the texture information of the image. In the defect detection part, filtered images from different levels are fused together and the constructed detection scheme is used to detect defects in fabric sample images with the same texture background as that of the template image. A filter selection method is also developed to select optimal filters to facilitate defect detection. The nov- elty of the method comes from the observation that a Gabor filter with finer resolutions than the fabric defects yarn can contribute very little for defect segmentation but need additional computational time. The proposed scheme is tested by using 78 homogeneous textile fabric images. The results exhibit ac- curate defect detections with lower false alarms than using the standard Gabor wavelets. Analysis of the computational complexity of the proposed detection scheme is derived, which shows that the scheme can be implemented in real time easily.en_US
dc.languageengen_US
dc.publisherAmerican Institute of Mathematical Sciences. The Journal's web site is located at http://aimsciences.org/journals/jimo/description.htmen_US
dc.relation.ispartofJournal of Industrial and Management Optimizationen_US
dc.subjectDefect Detectionen_US
dc.subjectIndustrial Inspectionen_US
dc.subjectMulti-Level Gabor Waveleten_US
dc.subjectWoven Fabricsen_US
dc.titleFabric defect detection using multi-level tuned-matched gabor filtersen_US
dc.typeArticleen_US
dc.identifier.emailMak, KL:makkl@hkucc.hku.hken_US
dc.identifier.emailYiu, KFC:cedric@hkucc.hku.hken_US
dc.identifier.authorityMak, KL=rp00154en_US
dc.identifier.authorityYiu, KFC=rp00206en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.3934/jimo.2012.8.325en_US
dc.identifier.scopuseid_2-s2.0-84861763158en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84861763158&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume8en_US
dc.identifier.issue2en_US
dc.identifier.spage325en_US
dc.identifier.epage341en_US
dc.identifier.isiWOS:000304007400004-
dc.publisher.placeUnited Statesen_US
dc.relation.projectA tensor-based decomposition technique for detecting textile fabric defect occurrence-
dc.identifier.scopusauthoridMak, KL=7102680226en_US
dc.identifier.scopusauthoridPeng, P=55237566500en_US
dc.identifier.scopusauthoridYiu, KFC=24802813000en_US

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