Name
Abstract | ||
|---|---|---|
A novel approach is proposed for measuring fabric texture orientations and recognizing weave patterns in this paper. Wavelet transform is suited for fabric image decomposition and Radon Transform is fit for line detection in fabric texture. Their excellent performances are put together to detect texture orientations in this study. Since different weave patterns have their own regular orientations in original image and sub-band images decomposed by Wavelet transform, these orientations features are extracted and used as LVQ inputs to achieve automatic recognition of fabric weave. The contribution of this algorithm is that it not only can identify fundamental fabric weaves but also can classify double layer and some derivative twill weaves such as angular twill and pointed twill. The experimental results show that the proposed method feasible and effective. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-16339-5_2 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Wavelet transform,fabric weave,texture orientation,neural network | Computer vision,Computer science,Learning vector quantization,Artificial intelligence,Artificial neural network,Radon transform,Wavelet transform,Double layer (surface science) | Conference |
Volume | Issue | ISSN |
106 | PART 2 | 1865-0929 |
Citations | PageRank | References |
2 | 0.68 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianqiang Shen | 1 | 236 | 17.86 |
| Xuan Zou | 2 | 19 | 3.91 |
| Fang Xu | 3 | 2 | 0.68 |
| Zhicong Xian | 4 | 2 | 0.68 |