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Fabric defect inspection using prior knowledge guided least squares regression

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Abstract

This paper proposes an unsupervised model to inspect various detects in fabric images with diverse textures. A fabric image with defects is usually composed of a relatively consistent background texture and some sparse defects, which can be represented as a low-rank matrix plus a sparse matrix in a certain feature space. The process is formulated as a least squares regression based subspace segmentation model, which is convex, smooth and can be solved efficiently. A simple and effective prior is also learnt from local texture features of the image itself. Instead of considering only the feature space’ s global structure, the local prior is incorporated with it seamlessly by the proposed subspace segmentation model to guide and improve the segmentation. Experiments on a variety of fabric images demonstrate the effectiveness and robustness of the proposed method. Compared with existing methods, our method is more robust and locates various defects more precisely.

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Acknowledgments

The authors would like to thank Huanhuan Zhang for her images of TILDA Textile Texture Database. Junjie Cao is supported by the NSFC China (61363048, 91230103). Zhijie Wen is supported by the NSFC China (11471208). Xiuping Liu is supported by the NSFC China (61173102, 61370143).

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Authors and Affiliations

  1. School of Mathematical Sciences, Dalian University of Technology, Dalian, China

    Junjie Cao, Jie Zhang, Nannan Wang & Xiuping Liu

  2. Department of Mathematics, College of Sciences, Shanghai University, Shanghai, China

    Zhijie Wen

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  1. Junjie Cao
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  2. Jie Zhang
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  3. Zhijie Wen
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  4. Nannan Wang
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  5. Xiuping Liu
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Correspondence to Zhijie Wen or Xiuping Liu.

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Cao, J., Zhang, J., Wen, Z. et al. Fabric defect inspection using prior knowledge guided least squares regression. Multimed Tools Appl 76, 4141–4157 (2017). https://doi.org/10.1007/s11042-015-3041-3

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  • DOI: https://doi.org/10.1007/s11042-015-3041-3

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