Name
Abstract | ||
|---|---|---|
This paper reports real-time full-pixel optical flow system that can simultaneously estimate pixelwise motion distributions of 512×512 images by accelerating the Lucas-Kanade method on a GPU-based high-speed vision system. In our optical flow system, the measurable dynamic range of the estimate optical flows is remarkably expanded by introducing a novel algorithm that can optimally select space intervals in optical flow estimation according to the estimated flow speed. Several experimental results for high-speed objects are shown to verify its effectiveness in high-frame-rate and real-time optical flow estimation. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2783449.2783501 | AIR |
Field | DocType | Citations |
Computer vision,Dynamic range,Machine vision,Computer science,Measure (mathematics),Flow velocity,Pixel,Artificial intelligence,Frame rate,Motion estimation,Optical flow | Conference | 0 |
PageRank | References | Authors |
0.34 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingyi Gu | 1 | 153 | 22.13 |
| Naoki Nakamura | 2 | 0 | 0.34 |
| Tadayoshi Aoyama | 3 | 69 | 28.58 |
| Takeshi Takaki | 4 | 222 | 38.04 |
| Idaku Ishii | 5 | 355 | 64.37 |