Name
Abstract | ||
|---|---|---|
One of the most important tasks in computer vision systems is the description of the local neighborhood around interest points. In RGB-D images, this task is usually performed by computing descriptors encoded as real-value vectors. The use of binary descriptors, like BRISK, BRIEF or ORB, has proven to be adequate to address this task accurately and efficiently. In this paper, we propose a novel binary pattern that encodes the shape around a given point in a RGB-D image with invariance to rotation and scale. This descriptor is contrasted to well-known state-of-the-art 3D descriptors, namely FPFH, Spin Images, SHOT, PFHRGB and CSHOT in order to test its actual performance on the RGB-D Objects dataset. The experiments performed show that the proposed binary pattern descriptor equals and even outperforms state-of-the-art 3D descriptors with a higher computational efficiency. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/WACV.2016.7477727 | WACV |
Field | DocType | ISSN |
Computer vision,Binary pattern,Invariant (physics),Pattern recognition,Computer science,Orb (optics),Object registration,Artificial intelligence,RGB color model,Binary number,Binary descriptor | Conference | 2472-6737 |
Citations | PageRank | References |
0 | 0.34 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristina Romero-González | 1 | 3 | 3.52 |
| Jesus Martínez-Gómez | 2 | 115 | 11.09 |
| Ismael García-varea | 3 | 275 | 36.16 |
| Luis Rodríguez-Ruiz | 4 | 8 | 2.89 |