Release Time:2016-11-15  Hits:

Indexed by: Journal Papers

Date of Publication: 2019-03-29

Journal: Multimedia Tools and Applications

Included Journals: SCI

Institution: Dalian university of technology

Page Number: 1-17

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 featur