Name
Abstract | ||
|---|---|---|
Matching keypoint pairs of different images is a basic task of computer vision. Most methods require customized extremum point schemes to obtain the coordinates of feature points with high confidence, which often need complex algorithmic design or a network with higher training difficulty and also ignore the possibility that flat regions can be used as candidate regions of matching points. In this paper, we design a region-based descriptor by combining the context features of a deep network. The new descriptor can give a robust representation of a point even in flat regions. By the new descriptor, we can obtain more high confidence matching points without extremum operation. The experimental results show that our proposed method achieves a performance comparable to state-of-the-art. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICIP42928.2021.9506462 | ICIP |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai Lv | 1 | 0 | 1.01 |
| Zongqing Lu | 2 | 209 | 26.18 |
| Qingmin Liao | 3 | 0 | 2.03 |