Name
Abstract | ||
|---|---|---|
Nowadays visual search is one of the most active branches of computer vision. It relies on finding invariant points inside images, describing them into features and then matching these features against a reference database to identify objects in the scene or the entire photo (environment). In this paper, we discuss an approach to feature matching that exploits the capabilities of modern GPUs to speed up the aforementioned and that keeps low the number of false matches. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.image.2012.06.002 | Sig. Proc.: Image Comm. |
Keywords | Field | DocType |
opencl-based feature matcher,entire photo,active branch,visual search,modern gpus,invariant point,computer vision,false match,reference database | Computer vision,Visual search,Computer science,Reference database,Exploit,Theoretical computer science,Feature matching,Artificial intelligence,Invariant (mathematics),Speedup | Journal |
Volume | Issue | ISSN |
28 | 4 | 0923-5965 |
Citations | PageRank | References |
3 | 0.44 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giovanni Condello | 1 | 3 | 0.44 |
| Paolo Pasteris | 2 | 3 | 0.44 |
| Danilo Pau | 3 | 20 | 9.74 |
| Mariagiovanna Sami | 4 | 314 | 39.98 |