Local Characterisation and Detection of Woven Fabric Texture Based on a Sparse Dictionary

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dc.contributor.authorWu, Ying
dc.contributor.authorWang, Ren
dc.contributor.authorLou, Lin
dc.contributor.authorWang, Lali
dc.contributor.authorWang, Jun
dc.contributor.organizationZhejiang Province Engineering Laboratory of Clothing Digital Technology, Hangzhou, Zhejiang, P.R. China
dc.contributor.organizationSchool of Fashion Design and Engineering, Zhejiang Sci-Tech University, Hangzhou, P.R. China
dc.contributor.organizationSchool of Economics and Management Zhejiang Sci-Tech University, Hangzhou, Zhejiang, P.R. China
dc.contributor.organizationKey Laboratory of Advanced Textile Materials and Manufacturing Technology, Zhejiang Sci-Tech University, P.R. China
dc.contributor.organizationZhejiang Provincial Key Laboratory of Fiber Materials and Manufacturing Technology, Zhejiang Sci-Tech University, P.R. China
dc.contributor.organizationCollege of Textiles, Donghua University, Shanghai, P.R. China
dc.contributor.organizationKey Laboratory of Textile Science & Technology, Ministry of Education Donghua University, Shanghai, P.R. China
dc.date.accessioned2025-05-26T10:50:13Z
dc.date.available2025-05-26T10:50:13Z
dc.date.issued2022-09-28
dc.description.abstractTo achieve enhanced accuracy of fabric representation and defect detection, an innovative approach using a sparse dictionary with small patches was used for fabric texture characterisation. The effectiveness of the algorithm proposed was tested through comprehensive characterisation by studying eight weave patterns: plain, twill, weft satin, warp satin, basket, honeycomb, compound twill, and diamond twill and detecting fabric defects. Firstly, the main parameters such as dictionary size, patch size, and cardinality T were optimised, and then 40 defect-free fabric samples were characterised by the algorithm proposed. Subsequently, the impact of the weave pattern was investigated based on the representation result and texture structure. Finally, defective fabrics were detected. The algorithm proposed is an alternative simple and scalable method to characterise fabric texture and detect textile defects in a single step without extracting features or prior information.en
dc.description.sponsorshipThis research was supported by the Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ18E030007; National Natural Science Foundation of China under Grant No. 52003245; Science Foundation of Zhejiang Sci-Tech University (ZSTU) [17072156-Y]; Key Laboratory of Advanced Textile Materials and Manufacturing Technology (Zhejiang Sci-Tech University), Ministry of Education, and Zhejiang Provincial Key Laboratory of Fiber Materials and Manufacturing Technology under Grant number 2019QN05; National Experimental Teaching Center of Clothing and National Virtual Simulation Experimental Teaching Center of Clothing Design [zx20212007].
dc.identifier.citationYing Wu, Ren Wang, Lin Lou, Lali Wang and Jun Wang. "Local Characterisation and Detection of Woven Fabric Texture Based on a Sparse Dictionary ". Fibres & Textiles in Eastern Europe Sciendo, 30, no. 3 (2022): 33-40. https://doi.org/10.2478/ftee-2022-0020
dc.identifier.doi10.2478/ftee-2022-0020
dc.identifier.issn1230-3666
dc.identifier.issn2300-7354
dc.identifier.urihttps://open.icm.edu.pl/handle/123456789/25898
dc.language.isoen
dc.publisherSciendo
dc.relation.ispartofseries30; 3
dc.rightsUznanie autorstwa-Użycie niekomercyjne-Bez utworów zależnych 3.0 Unporteden
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/
dc.sourceFibres & Textiles in Eastern Europe
dc.subjectfabric texture representationen
dc.subjectsparse representationen
dc.subjectweave repeaten
dc.subjectdefect detectionen
dc.subjectdictionary learningen
dc.titleLocal Characterisation and Detection of Woven Fabric Texture Based on a Sparse Dictionaryen
dc.typearticle
dc.type.versionpublishedVersion
person.identifier.orcidWu, Ying [0000-0001-6581-9816]
person.identifier.orcidLou, Lin [0000-0003-4856-9944]
person.identifier.orcidWang, Jun [0000-0002-5655-6070]
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