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Multilinear principal component analysis-based tensor decomposition for fabric weave pattern recognition from high-dimensional streaming data

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Abstract

Modern textile industry integrates video sensors with automated fabric reeling systems for real-time fabric weave pattern inspection. This automation system lessens the human-vision-based cognitive load and improves fabric weave pattern inspection work. However, this automation system poses a unique challenge, particularly when dealing with high-dimensional streaming data from highly precision digital microscope cameras. The complexity arises from the continuous acquisition and management of such high-dimensional streaming video data. Considering the challenges posed by dimensionality reduction in high-dimensional data, this study employs multilinear principal component analysis (MPCA)-based tensor decomposition, a statistical technique designed to effectively reduce high-dimensional datasets into low-dimensional features. This paper proposes an innovative method for fabric weave pattern recognition (FWPR) by leveraging MPCA-based tensor decomposition to extract low-dimensional features from the high-dimensional fabric’s surface texture descriptor tensor (STDT). This proposed method replicates fabric pattern monitoring in automated fabric reeling systems by integrating a digital microscope camera to capture high-dimensional streaming video data from fabric surface texture features. Subsequently high-dimensional video data is converted into sequential image frames representing different fabric weave patterns. These image frames are processed with local binary pattern (LBP) and gray-level co-occurrence matrix (GLCM) methods to aggregate fabric’s surface pattern features and construct the high-dimensional STDT. This STDT is subsequently decomposed into low-dimensional features by leveraging MPCA, resulting in an impressive 99.99% reduction in dimension. A supervised machine learning method utilizes the extracted low-dimensional features to enable FWPR, demonstrating superiority of the proposed method over the benchmark methods in evaluation.

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Acknowledgements

The authors express their gratitude to the Department of Human Sciences at Mississippi State University for their invaluable technical support and for facilitating the acquisition of fabric swatches, which were instrumental in conducting this research.

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Authors and Affiliations

  1. Department of Engineering and Industrial Professions, University of North Alabama, Florence, AL, 35632, USA

    Abdullah Al Mamun, Md Abu Sayeed Shohag & Wael Al-Kouz

  2. Department of Textile Development and Marketing, Fashion Institute of Technology, New York City, NY, 10001-5992, USA

    Md Imranul Islam

  3. Department of Human Ecology, University of Alberta, Edmonton, AB, T6G 2N1, Canada

    KM Abdun Noor

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Contributions

Abdullah Al Mamun: conceptualization, supervision, methodology, statistical computing, original draft preparation, review, and editing. Md. Imranul Islam: conceptualization, methodology, original draft preparation, and supervision. Md Abu Sayeed Shohag: methodology, statistical computing, review, and editing. Wael Al-Kouz: methodology, supervision, review, and editing. K. M. Abdun Noor: methodology, review, and editing.

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Al Mamun, A., Islam, M.I., Shohag, M.A.S. et al. Multilinear principal component analysis-based tensor decomposition for fabric weave pattern recognition from high-dimensional streaming data. Pattern Anal Applic 27, 100 (2024). https://doi.org/10.1007/s10044-024-01318-4

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