点击次数：
论文类型：Journal Papers
发表时间：2019-03-29
发表刊物：Multimedia Tools and Applications
收录刊物：SCI
所属单位：Dalian university of technology
页面范围：1-17
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 featur