Name
Abstract | ||
|---|---|---|
A real-time feature point matching algorithm is introduced. It extracts vector-based ColourFAST feature strength and direction measures from the colour channels of the pixels in an image. This information is combined with the relative locations of the feature points to provide frame-by-frame scale and rotation invariant matching. The resulting algorithm is specifically designed for high throughput and optimised for GPU pipelining on embedded devices. Results are given showing high-framerate matching is achieved on 720p resolution images. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/IVCNZ.2015.7761518 | 2015 International Conference on Image and Vision Computing New Zealand (IVCNZ) |
Keywords | Field | DocType |
GPU-accelerated feature point matching algorithm,extended ColourFAST descriptors,vector-based ColourFAST feature strength,rotation invariant matching,GPU pipelining,embedded devices,high-frame rate matching | Template matching,Computer vision,Algorithm design,Pattern recognition,Feature detection (computer vision),Computer science,Feature (computer vision),Feature extraction,Artificial intelligence,Pixel,Invariant (mathematics),Cognitive neuroscience of visual object recognition | Conference |
ISSN | ISBN | Citations |
2151-2191 | 978-1-5090-0358-7 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eleanor Da Fonseca | 1 | 0 | 0.34 |
| Andrew Ensor | 2 | 10 | 4.23 |
| Seth Hall | 3 | 30 | 3.37 |