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dc.contributor.authorLuo, Qiwu
dc.contributor.authorFang, Xiaoxin
dc.contributor.authorSun, Yichuang
dc.contributor.authorLiu, Li
dc.contributor.authorAi, Jiaqiu
dc.contributor.authorYang, Chunhua
dc.contributor.authorSimpson, Oluyomi
dc.date.accessioned2019-03-19T15:08:28Z
dc.date.available2019-03-19T15:08:28Z
dc.date.issued2019-02-11
dc.identifier.citationLuo , Q , Fang , X , Sun , Y , Liu , L , Ai , J , Yang , C & Simpson , O 2019 , ' Surface Defect Classification for Hot-Rolled Steel Strips by Selectively Dominant Local Binary Patterns ' , IEEE Access , vol. 7 , pp. 23488 - 23499 . https://doi.org/10.1109/ACCESS.2019.2898215
dc.identifier.issn2169-3536
dc.identifier.otherPURE: 16419181
dc.identifier.otherPURE UUID: 14aa526c-38cf-4955-8cdd-b742f9a0cb96
dc.identifier.otherScopus: 85062722382
dc.identifier.urihttp://hdl.handle.net/2299/21206
dc.description.abstractDevelopments in defect descriptors and computer vision-based algorithms for automatic optical inspection (AOI) allows for further development in image-based measurements. Defect classification is a vital part of an optical-imaging-based surface quality measuring instrument. The high-speed production rhythm of hot continuous rolling requires an ultra-rapid response to every component as well as algorithms in AOI instrument. In this paper, a simple, fast, yet robust texture descriptor, namely selectively dominant local binary patterns (SDLBPs), is proposed for defect classification. First, an intelligent searching algorithm with a quantitative thresholding mechanism is built to excavate the dominant non-uniform patterns (DNUPs). Second, two convertible schemes of pattern code mapping are developed for binary encoding of all uniform patterns and DNUPs. Third, feature extraction is carried out under SDLBP framework. Finally, an adaptive region weighting method is built for further strengthening the original nearest neighbor classifier in the feature matching stage. The extensive experiments carried out on an open texture database (Outex) and an actual surface defect database (Dragon) indicates that our proposed SDLBP yields promising performance on both classification accuracy and time efficiencyen
dc.format.extent11
dc.language.isoeng
dc.relation.ispartofIEEE Access
dc.subjectAutomatic optical inspection (AOI) image classification local binary patterns (LBP) steel industry
dc.subjectsurface texture
dc.subjectComputer Science(all)
dc.subjectMaterials Science(all)
dc.subjectEngineering(all)
dc.titleSurface Defect Classification for Hot-Rolled Steel Strips by Selectively Dominant Local Binary Patternsen
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionSchool of Engineering and Technology
dc.contributor.institutionSmart Electronics Devices and Networks
dc.contributor.institutionRadio and Mobile Communication Systems
dc.contributor.institutionUniversity of Hertfordshire
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85062722382&partnerID=8YFLogxK
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1109/ACCESS.2019.2898215
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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