Name
Abstract | ||
|---|---|---|
Processing visual content in images and videos is a challenging task associated with the development of modern computer vision. Because salient point approaches can represent distinctive and affine invariant points in images, many approaches have been proposed over the past decade. Each method has particular advantages and limitations and may be appropriate in different contexts. In this paper we evaluate the performance of a wide set of salient point detectors and descriptors. We begin by comparing diverse salient point algorithms (SIFT, SURF, BRIEF, ORB, FREAK, BRISK, STAR, GFTT and FAST) with regard to repeatability, recall and precision and then move to accuracy and stability in real-time video tracking. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2502081.2502179 | ACM Multimedia 2001 |
Keywords | Field | DocType |
different context,salient point approach,past decade,challenging task,salient point method,real-time video tracking,particular advantage,diverse salient point algorithm,affine invariant point,modern computer vision,salient point detector,evaluation,video tracking | Computer vision,Scale-invariant feature transform,FREAK,Computer science,Orb (optics),Precision and recall,Video tracking,Affine invariant,Artificial intelligence,Salient | Conference |
Citations | PageRank | References |
1 | 0.37 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Song Wu | 1 | 90 | 5.58 |
| Michael S. Lew | 2 | 2742 | 166.02 |