Name
Abstract | ||
|---|---|---|
In this paper, we analyze the key factors underlying the implementation, evaluation, and optimization of image processing and computer vision algorithms on embedded GPU using OpenGL ES 2.0 shader model. First, we present the characteristics of the embedded GPU and its inherent advantage when compared to embedded CPU. Additionally, we propose techniques to achieve increased performance with optimized shader design. To show the effectiveness of the proposed techniques, we employ cartoon-style non-photorealistic rendering (NPR), speeded-up robust feature (SURF) detection, and stereo matching as our example algorithms. Performance is evaluated in terms of the execution time and speed-up achieved in comparison with the implementation on embedded CPU. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1587/transinf.E95.D.1475 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
embedded GPU, GPGPU, image processing, OpenGL ES 2.0, NPR, SURF, stereo matching | Stereo matching,Computer vision,Computer graphics (images),Computer science,Image processing,Artificial intelligence,General-purpose computing on graphics processing units,Digital image processing | Journal |
Volume | Issue | ISSN |
E95D | 5 | 1745-1361 |
Citations | PageRank | References |
15 | 0.84 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nitin Singhal | 1 | 113 | 10.55 |
| Jin Woo Yoo | 2 | 32 | 1.78 |
| Ho Yeol Choi | 3 | 20 | 1.24 |
| In Kyu Park | 4 | 316 | 35.97 |