Name
Abstract | ||
|---|---|---|
Template matching methods have been widely utilized to detect fabric defects in textile quality control. In this paper, a
novel approach is proposed to design a flexible classifier for distinguishing flaws from twill fabrics by statistically learning
from the normal fabric texture. Statistical information of natural and normal texture of the fabric can be extracted via collecting
and analyzing the gray image. On the basis of this, both judging threshold and template are acquired and updated adaptively
in real-time according to the real textures of fabric, which promises more flexibility and universality. The algorithms are
experimented with images of fault free and faulty textile samples. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s11633-010-0086-7 | International Journal of Automation and Computing |
Keywords | DocType | Volume |
threshold self-learning.,template matching,image processing,fabric flaw detection,adaptive template,quality control,real time | Journal | 7 |
Issue | ISSN | Citations |
1 | 17518520 | 2 |
PageRank | References | Authors |
0.54 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li-Wei Han | 1 | 48 | 3.68 |
| De Xu | 2 | 62 | 10.73 |
| De Xu | 3 | 142 | 25.04 |
| De Xu | 4 | 142 | 25.04 |