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    Section I

    Defect Detection in Woven Fabrics by Analysis of Co-occurrence Texture Features as a Function of Gray-level Quantization and Window Size

    Authors:

    P. S. H. Pallemulla ,

    University of Moratuwa, LK
    About P. S. H.

    Research Assistant

     

    AMIE(SL), B.Sc. Eng. Hons (KDU)

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    S. J. Sooriyaarachchi,

    University of Moratuwa, LK
    About S. J.

    Senior Lecturer in Computer Science and Engineering

     

    AMIE(SL), MCS(SL), AMTILP, B.Sc. Eng. Hons (Peradeniya), M.Sc. (Moratuwa), Ph.D. (Moratuwa

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    C. R. de Silva,

    University of Moratuwa, LK
    About C. R.

    Senior Lecturer in Computer Science and Engineering

     

    C. Eng., MIE(SL), B.Sc. Eng. Hons (Moratuwa), M.Eng. (NTU), Ph.D. (NTU)

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    C. D. Gamage

    University of Moratuwa, LK
    About C. D.

    Senior Lecturer in Computer Science and Engineering

     

    C. Eng., MIE(SL), B.Sc. Eng. Hons (Moratuwa), M.Eng. (AIT), Ph.D. (Monash)

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    Abstract

    In this experimental research, the effects of gray-level quantization and tiling window size on 22 gray-level co-occurrence matrix features were investigated in the context of automated woven fabric defect detection. A dataset comprising 1426 128×128 images was used, in which defective and the defect-free images were split in a 50:50 ratio. Experiments were carried out with seven quantization levels (

    Keywords: Fabric defect detection, GLCM, Gray-level quantization, Window size
    How to Cite: Pallemulla, P.S.H., Sooriyaarachchi, S.J., de Silva, C.R. and Gamage, C.D., 2021. Defect Detection in Woven Fabrics by Analysis of Co-occurrence Texture Features as a Function of Gray-level Quantization and Window Size. Engineer: Journal of the Institution of Engineers, Sri Lanka, 54(4), pp.55–64. DOI: http://doi.org/10.4038/engineer.v54i4.7470
    Published on 30 Dec 2021.
    Peer Reviewed

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