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

Author

Listed:
  • 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)

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..

Suggested Citation

  • Ruru Pan & Charles Kumah & Ning Zhang, 2017. "Analysis of Printed Fabric Pattern Segmentation Based on Unsupervised Clustering of K-Means Algorithm," Current Trends in Fashion Technology & Textile Engineering, Juniper Publishers Inc., vol. 1(4), pages 79-81, November.
    • Handle: RePEc:adp:ctftte:v:1:y:2017:i:4:p:79-81
    DOI: 10.19080/CTFTTE.2017.01.55568
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