Name
Abstract | ||
|---|---|---|
We investigate on local reference frames (LRF) deployed with 3D descriptors to achieve invariance to objects' pose. We address the task of matching together partial views of surfaces and propose an experimental study on a large corpus of real data which allows for clearly ranking existing LRF proposals based on their repeatability. Then, drawing inspiration from analysis of the experimental findings, we formulate a new proposal which, in particular, peculiarly includes a procedure aimed at estimating a repeatable LRF also at border features, which is very important when matching partial views of surfaces. Experiments show that the new proposal neatly outperforms existing methods in terms of repeatability, is computationally very efficient and provide relevant benefits in practical applications. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICCV.2011.6126503 | Computer Vision |
Keywords | Field | DocType |
image matching,3D descriptors,local reference frame,partial shape matching | Reference frame,Computer vision,Ranking,Pattern recognition,Invariant (physics),Image matching,Computer science,Local reference frame,Artificial intelligence,Repeatability | Conference |
Volume | Issue | ISSN |
2011 | 1 | 1550-5499 |
ISBN | Citations | PageRank |
978-1-4577-1101-5 | 35 | 0.86 |
References | Authors | |
14 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alioscia Petrelli | 1 | 82 | 4.38 |
| Luigi Di Stefano | 2 | 197 | 11.89 |