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Automatic color pattern recognition of multispectral printed fabric images

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Abstract

Printed fabrics often have rich colors and variable patterns in different sizes and shapes, which make it difficult to achieve accurate pattern recognition and color measurement using traditional spectrophotometers and digital cameras. This paper develops a grid-based density peaks clustering (GDPC) algorithm to automatically recognize patterns and extract colors of multispectral images of printed fabrics. The multispectral images captured by a self-developed multispectral imaging system is firstly converted into color images in CIELAB color space and three principal components are calculated by applying principal component analysis to reduce the dimensions of the multispectral images. During the multispectral image processing, the noise pixels are removed by calculating the local stability of each pixel, and then the remaining stable pixels are clustered using proposed GDPC algorithm based on three CIELAB color channels and three principal components. Compared with widely-used color clustering algorithms, the proposed GDPC algorithm can recognize the color patterns from more intricate multispectral printed fabric images with higher accuracy and less computational time.

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Acknowledgements

We would like to acknowledge the Innovation and Technology Fund, Ref no. ITP/048/13TP. from the Innovation and Technology Commission of the Hong Kong SAR Government.

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Author notes

  1. Zhang Jie and Yao Pengpeng contributed equally to this work.

Authors and Affiliations

  1. School of Design, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China

    Jie Zhang

  2. Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China

    Pengpeng Yao, Hochung Wu & John H. Xin

Authors
  1. Jie Zhang
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  2. Pengpeng Yao
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  3. Hochung Wu
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  4. John H. Xin
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Correspondence to John H. Xin.

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Zhang, J., Yao, P., Wu, H. et al. Automatic color pattern recognition of multispectral printed fabric images. J Intell Manuf 34, 2747–2763 (2023). https://doi.org/10.1007/s10845-022-01947-8

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  • DOI: https://doi.org/10.1007/s10845-022-01947-8

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