Name
Abstract | ||
|---|---|---|
We introduce a simple texture synthesis method based on support vector machines (SVM). Although SVM has been effectively used for various pattern recognition tasks, there is no report available on directly applying SVM for texture synthesis. The advantage of using SVM is that the sample can be simply modeled by a linear model and is not required during the synthesis stage. In addition, the method can be further extended to synthesize 3D surface texture or bidirectional texture functions. Our experimental results show that the method can successfully model and synthesize semi or highly structured textures, which can be difficult subjects for previous texture synthesis methods based on parametric models. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICPR.2008.4761785 | ICPR |
Keywords | Field | DocType |
linear model,pattern recognition,surface texture,feature extraction,support vector machine,bidirectional texture function,3d surface texture,image texture,support vector machines,texture synthesis,parametric model | Computer vision,Texture compression,Parametric model,Pattern recognition,Computer science,Linear model,Image texture,Support vector machine,Feature extraction,Artificial intelligence,Texture synthesis,Texture filtering | Conference |
ISSN | ISBN | Citations |
1051-4651 E-ISBN : 978-1-4244-2175-6 | 978-1-4244-2175-6 | 0 |
PageRank | References | Authors |
0.34 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junyu Dong | 1 | 393 | 77.68 |
| Yuanxu Duan | 2 | 0 | 0.68 |
| Guimei Sun | 3 | 5 | 1.10 |
| Lin Qi | 4 | 18 | 6.47 |