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论文类型：期刊论文
发表时间：2017-02-01
发表刊物：MULTIMEDIA TOOLS AND APPLICATIONS
收录刊物：SCIE、EI
卷号：76
期号：3
页面范围：4141-4157
ISSN号：1380-7501
关键字：Low-rank; Fabric defect detection; Prior knowledge; Least squares regression
摘要：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.