Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11042-015-3041-3 | Multimedia Tools Appl. |
Keywords | DocType | Volume |
Low-rank, Fabric defect detection, Prior knowledge, Least squares regression | Journal | 76 |
Issue | ISSN | Citations |
3 | 1573-7721 | 8 |
PageRank | References | Authors |
0.45 | 20 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junjie Cao | 1 | 66 | 2.71 |
| Jie Zhang | 2 | 112 | 7.99 |
| Zhijie Wen | 3 | 39 | 7.14 |
| nannan wang | 4 | 8 | 0.45 |
| XiuPing Liu | 5 | 112 | 7.97 |