Analysis of Printed Fabric Pattern Segmentation Based on Unsupervised Clustering of K-Means Algorithm

Ruru Pan, Charles Kumah and Ning Zhang
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Ruru Pan: School of Textiles and Clothing, Jiangnan University, Wuxi, Jiangsu, China
Charles Kumah: School of Textiles and Clothing, Jiangnan University, Wuxi, Jiangsu, China
Ning Zhang: School of Textiles and Clothing, Jiangnan University, Wuxi, Jiangsu, China

Current Trends in Fashion Technology & Textile Engineering, 2017, vol. 1, issue 4, 79-81

Abstract: Fabric segmentation has recently increased widely in diverse fields of computer technology of which printed fabric pattern is of no exception. This is due to the fact original printed fabric is just inadequate to extract suitable information for further applications. In textile industry, automation inspection for pattern recognition helps not only to improve fabric quality but also to reduce cost and wastage of time since human inspection is not 100% guaranteed. Printed fabric samples are washed, ironed, scanned and then preprocessed with median filter and segmented by k-means clustering algorithm..

Keywords: fashion technology trends; fashion technology advances; fashion and technology articles; fashion technology books pdf; textile science engineering journal; textile science; open access; juniper publishres; high impact journals; online journals publishers; open access journals list (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:adp:ctftte:v:1:y:2017:i:4:p:79-81

DOI: 10.19080/CTFTTE.2017.01.55568

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