Name
Abstract | ||
|---|---|---|
This paper presents GPU(Graphics Processing Unit) implementation of a clustering based image registration method. Since the image registration is an important process in image analysis tasks such as image restoration and image fusion, fast image registration can improve the overall application execution speed. Recently, the commodity GPU is being used in not only 3D graphics rendering but also in general-purpose computation due to an increase in the good price/performance ratio and hardware programmability as well as the huge computing power and speed of the GPU. We implemented clustering-based image registration method on GPU using only transformation of texture coordinations in vertex program and re-sampling in fragment program. Finally, GPU-based image registration speed up nearly 50 percent compared with CPU. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-87442-3_61 | ICIC (1) |
Keywords | Field | DocType |
clustering-based image registration method,image registration method,fast image registration,gpu implementation,image registration,image analysis task,image fusion,gpu-based image registration speed,image restoration,commodity gpu,overall application execution speed,image analysis,3d graphics | Computer vision,Computer graphics (images),Feature detection (computer vision),Computer science,Image processing,Real-time computer graphics,Artificial intelligence,Image restoration,Cluster analysis,Rendering (computer graphics),Digital image processing,Image registration | Conference |
Volume | ISSN | Citations |
5226 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seung-Hun Yoo | 1 | 16 | 3.69 |
| Yun-Seok Lee | 2 | 28 | 4.38 |
| Sung-Up Jo | 3 | 11 | 2.43 |
| Chang-Sung Jeong | 4 | 172 | 35.88 |