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Indexed by:期刊论文

Date of Publication:2017-02-01

Journal:MULTIMEDIA TOOLS AND APPLICATIONS

Included Journals:SCIE、EI

Volume:76

Issue:3

Page Number:4141-4157

ISSN No.:1380-7501

Key Words:Low-rank; Fabric defect detection; Prior knowledge; Least squares regression

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.