Oriented FAST and rotated BRIEF
 | This article includes a list of general references, but it lacks sufficient corresponding inline citations. (June 2014) |
| Feature detection |
|---|
| Edge detection |
| Corner detection |
| Blob detection |
| Ridge detection |
| Hough transform |
| Structure tensor |
| Affine invariant feature detection |
| Feature description |
| Scale space |
Oriented FAST and rotated BRIEF (ORB) is a fast robust local feature detector, first presented by Ethan Rublee et al. in 2011,[1] that can be used in computer vision tasks like object recognition or 3D reconstruction. It is based on the FAST keypoint detector and a modified version of the visual descriptor BRIEF (Binary Robust Independent Elementary Features). Its aim is to provide a fast and efficient alternative to SIFT.
See also[edit]
- Scale-invariant feature transform (SIFT)
- Gradient Location and Orientation Histogram
- LESH - Local Energy based Shape Histogram
- Blob detection
- Feature detection (computer vision)
References[edit]
- ^ a b Rublee, Ethan; Rabaud, Vincent; Konolige, Kurt; Bradski, Gary (2011). "ORB: an efficient alternative to SIFT or SURF" (PDF). IEEE International Conference on Computer Vision (ICCV). (registration required)