Name
Abstract | ||
|---|---|---|
RANSAC is a popular robust model estimation algorithm in various computer vision applications. However, the speed of RANSAC declines dramatically as the inlier rate of the measurements decreases. In this paper, a novel Adaptively Ranked Sample Consensus(ARSAC) algorithm is presented to boost the speed and robustness of RANSAC. The algorithm adopts non-uniform sampling based on the ranked measurements to speed up the sampling process. Instead of a fixed measurement ranking, we design an adaptive scheme which updates the ranking of the measurements, to incorporate high quality measurements into sample at high priority. At the same time, a geometric constraint is proposed during sampling process to select measurements with scattered distribution in images, which could alleviate degenerate cases in epipolar geometry estimation. Experiments on both synthetic and real-world data demonstrate the superiority in efficiency and robustness of the proposed algorithm compared to the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.neucom.2018.02.103 | Neurocomputing |
Keywords | Field | DocType |
RANSAC,Robust model estimation,Efficiency,Adaptively ranked measurements,Non-uniform sampling,Geometric constraint | Sampling process,Epipolar geometry,Pattern recognition,Ranking,RANSAC,Robustness (computer science),Sampling (statistics),Artificial intelligence,Mathematics,Speedup | Journal |
Volume | ISSN | Citations |
328 | 0925-2312 | 2 |
PageRank | References | Authors |
0.36 | 16 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Li | 1 | 6 | 2.08 |
| Jinqiu Sun | 2 | 33 | 8.27 |
| Dong Gong | 3 | 96 | 12.24 |
| Yu Zhu | 4 | 88 | 12.65 |
| Haisen Li | 5 | 49 | 5.47 |
| Yanning Zhang | 6 | 1613 | 176.32 |