We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Fabric defect fetection via weighted low-rank decomposition and Laplacian regularization.
- Authors
Ji, Xuan; Liang, Jiuzhen; Di, Lan; Xia, Yunfei; Hou, Zhenjie; Huan, Zhan; Huan, Yuxi
- Abstract
Low-rank decomposition models have potential for fabric defect detection, where a feature matrix is decomposed into a low-rank matrix that corresponding to repeated texture structure and a sparse matrix that represent defective regions. Two limitations, however, still exist. First, previous work might fail to detect some large homogeneous defective block. Second, when the background and defective regions are relatively coherent or the texture of fabric image is complex, it is difficult to use previous methods to separate them. To deal with these problems, a new weighted low-rank decomposition model with Laplace regularization (WLRL) is proposed in this paper: (1) a weighted low-rank decomposition model that can decompose the original image into background and defective regions, and (2) a Laplace regularization that can enlarge the distance between the background and the defective regions. The performance of the proposed method WLRL is evaluated on the box- and star-patterned fabric databases, and superior results are shown compared with state-of-the-art methods, that is, 98.70% ACC (accuracy) and 72.83% TPR (true positive rate) for box-patterned fabrics, 99.09% ACC (accuracy) and 83.63% TPR (true positive rate) for star-patterned fabrics.
- Subjects
LOW-rank matrices; MATHEMATICAL regularization; SPARSE matrices; MATHEMATICAL decomposition; TEXTILE defects; IMAGE fusion
- Publication
Journal of Engineered Fabrics & Fibers (JEFF), 2021, Vol 16, p1
- ISSN
1558-9250
- Publication type
Article
- DOI
10.1177/1558925020957654