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Article: Performance evaluation for motif-based patterned texture defect detection

TitlePerformance evaluation for motif-based patterned texture defect detection
Authors
KeywordsDefect detection
Motif
Patterned fabric
Texture analysis
Wallpaper group
Issue Date2010
PublisherIEEE
Citation
Ieee Transactions On Automation Science And Engineering, 2010, v. 7 n. 1, p. 58-72 How to Cite?
AbstractThis paper carries an extensive evaluation on the performance of a generalized motif-based method for detecting defects in 16 out of 17 wallpaper groups in 2-D patterned texture. The motif-based method evolves from the concept that every wallpaper group is defined by a lattice, which contains a further constituent-motif. It utilizes the symmetry properties of motifs to calculate the energy of moving subtraction and its variance among motifs. Decision boundaries are determined by learning the distribution of those values among the defect-free and defective patterns in the energy-variance space. In this paper, shape transform for irregular motif has been demonstrated according to the three basic motif shapes: rectangle, triangle, and parallelogram. An error analysis for the misclassifications has also been delivered. In the database of fabrics and other patterned textures, a total of 381 defect-free lattices are used for formulation of boundaries while further 340 defect-free and 233 defective lattices are for testing. The motif-based method has a consistent result and reaches adetection success rate of 93.86%. Note to Practitioners-This paper is motivated by the need to develop a generalized approach that can detect defects on most of the 2-D patterned textures defined so far. It proposes a novel motif-based defect detection method for 16 out of 17 wallpapergroups. A new concept called energy of moving subtraction is defined using norm metric measurement between a collection of circular shift matrices of motif and itself. Together with its variance, an energy-variance space is defined where decision boundaries are drawn for classifying defective and defect-free motifs. The method has been evaluated by two categories of patterned textures. The first category is produced from patterned fabric samples from p2, pmm, p4m, pm, and cm groups. The second category is produced from various patterned texture samples from p4, pg, pmg, cmm, p4g, pgg, p31m, p6, p6m, and p3m1 groups. For the former, a total of 280 defect-free lattices samples are used for deriving the decision boundaries, and further 340 defect-free and 206 defective lattices are used for evaluation. The detection success rate is found to be 93.92%. For the latter, they are the images from painting, tile, ornament, painted porcelain, vessel, earthenware, mat, tapestry, cloth, and wall tiling. A total of 101 defect-free lattices are acquired and 27 defective lattices are used for defect detection. The detection success rate for the second category is 92.59%. An overall detection success rate of 93.86% is achieved for the motif-based method. No other (generalized) approach was able to handle such a large number of wallpaper groups of 2-D patterned textures, and hence this result outperforms all other previously published approaches. This result contributes to the quality assurance of production of textile, wallpaper, ceramics, ornament, and tile. © 2008 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/58825
ISSN
2015 Impact Factor: 2.696
2015 SCImago Journal Rankings: 1.832
ISI Accession Number ID
Funding AgencyGrant Number
CRCG, University of Hong Kong
Funding Information:

This work was supported in part by CRCG, University of Hong Kong.

References

 

DC FieldValueLanguage
dc.contributor.authorNgan, HYTen_HK
dc.contributor.authorPang, GKHen_HK
dc.contributor.authorYung, NHCen_HK
dc.date.accessioned2010-05-31T03:37:34Z-
dc.date.available2010-05-31T03:37:34Z-
dc.date.issued2010en_HK
dc.identifier.citationIeee Transactions On Automation Science And Engineering, 2010, v. 7 n. 1, p. 58-72en_HK
dc.identifier.issn1545-5955en_HK
dc.identifier.urihttp://hdl.handle.net/10722/58825-
dc.description.abstractThis paper carries an extensive evaluation on the performance of a generalized motif-based method for detecting defects in 16 out of 17 wallpaper groups in 2-D patterned texture. The motif-based method evolves from the concept that every wallpaper group is defined by a lattice, which contains a further constituent-motif. It utilizes the symmetry properties of motifs to calculate the energy of moving subtraction and its variance among motifs. Decision boundaries are determined by learning the distribution of those values among the defect-free and defective patterns in the energy-variance space. In this paper, shape transform for irregular motif has been demonstrated according to the three basic motif shapes: rectangle, triangle, and parallelogram. An error analysis for the misclassifications has also been delivered. In the database of fabrics and other patterned textures, a total of 381 defect-free lattices are used for formulation of boundaries while further 340 defect-free and 233 defective lattices are for testing. The motif-based method has a consistent result and reaches adetection success rate of 93.86%. Note to Practitioners-This paper is motivated by the need to develop a generalized approach that can detect defects on most of the 2-D patterned textures defined so far. It proposes a novel motif-based defect detection method for 16 out of 17 wallpapergroups. A new concept called energy of moving subtraction is defined using norm metric measurement between a collection of circular shift matrices of motif and itself. Together with its variance, an energy-variance space is defined where decision boundaries are drawn for classifying defective and defect-free motifs. The method has been evaluated by two categories of patterned textures. The first category is produced from patterned fabric samples from p2, pmm, p4m, pm, and cm groups. The second category is produced from various patterned texture samples from p4, pg, pmg, cmm, p4g, pgg, p31m, p6, p6m, and p3m1 groups. For the former, a total of 280 defect-free lattices samples are used for deriving the decision boundaries, and further 340 defect-free and 206 defective lattices are used for evaluation. The detection success rate is found to be 93.92%. For the latter, they are the images from painting, tile, ornament, painted porcelain, vessel, earthenware, mat, tapestry, cloth, and wall tiling. A total of 101 defect-free lattices are acquired and 27 defective lattices are used for defect detection. The detection success rate for the second category is 92.59%. An overall detection success rate of 93.86% is achieved for the motif-based method. No other (generalized) approach was able to handle such a large number of wallpaper groups of 2-D patterned textures, and hence this result outperforms all other previously published approaches. This result contributes to the quality assurance of production of textile, wallpaper, ceramics, ornament, and tile. © 2008 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEEen_HK
dc.relation.ispartofIEEE Transactions on Automation Science and Engineeringen_HK
dc.rights©2008 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.subjectDefect detectionen_HK
dc.subjectMotifen_HK
dc.subjectPatterned fabricen_HK
dc.subjectTexture analysisen_HK
dc.subjectWallpaper groupen_HK
dc.titlePerformance evaluation for motif-based patterned texture defect detectionen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1545-5955&volume=&spage=&epage=&date=2008&atitle=Performance+Evaluation+for+Motif-based+Patterned+Texture+Defect+Detectionen_HK
dc.identifier.emailPang, GKH:gpang@eee.hku.hken_HK
dc.identifier.emailYung, NHC:nyung@eee.hku.hken_HK
dc.identifier.authorityPang, GKH=rp00162en_HK
dc.identifier.authorityYung, NHC=rp00226en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TASE.2008.2005418en_HK
dc.identifier.scopuseid_2-s2.0-73849089508en_HK
dc.identifier.hkuros152798en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-73849089508&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume7en_HK
dc.identifier.issue1en_HK
dc.identifier.spage58en_HK
dc.identifier.epage72en_HK
dc.identifier.isiWOS:000273133300006-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridNgan, HYT=7102173824en_HK
dc.identifier.scopusauthoridPang, GKH=7103393283en_HK
dc.identifier.scopusauthoridYung, NHC=7003473369en_HK
dc.identifier.citeulike6019050-

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