Name
Abstract | ||
|---|---|---|
Some of the best performing local stereo-matching approaches use cross-bilateral filters for proper cost aggregation. The recent attempts have been directed toward efficient approximations of such filter aimed at higher speed. In this paper, we suggest a simple yet efficient coarse-to-fine cost volume aggregation scheme, which employs pyramidal decomposition of the cost volume followed by edge-avoiding reconstruction and aggregation. The scheme substantially reduces the computational complexity while providing fair quality of the estimated disparity maps compared to other approximated bilateral filtering schemes. In fact, the speed of the proposed technique is comparable with the speed of fixed kernel aggregation implemented through integral images. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/VCIP.2014.7051615 | VCIP |
Keywords | Field | DocType |
fixed kernel aggregation,edge-avoiding reconstruction,disparity maps,stereo-matching,hierarchical cost volume aggregation,integral images,image reconstruction,bilateral filtering schemes,computational complexity,aggregation,decomposition,image filtering,cross-bilateral filters,stereo image processing,coarse-to-fine cost volume aggregation scheme,pyramidal decomposition | Kernel (linear algebra),Stereo matching,Computer vision,Computer science,Approximations of π,Cost aggregation,Artificial intelligence,Bilateral filter,Computational complexity theory | Conference |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sergey Smirnov | 1 | 9 | 2.12 |
| Atanas P. Gotchev | 2 | 223 | 38.55 |