Name
Title | ||
|---|---|---|
Accurate estimation of feature points based on individual projective plane in video sequence |
Abstract | ||
|---|---|---|
The stability and quantity of feature matching in video sequence is one of the key issues for feature tracking and some relevant applications. The existing matching methods are based on feature detection, which is usually affected by illumination conditions, noise or occlusions, and this will directly influence matching results. In this paper, we propose an accurate prediction method for interest point estimation in video sequence by extracting the stable mapping for each undetected point in its suitable projective plane, which is based on coplanar feature points that have already been detected in adjacent frames. The proposed prediction method breaks the limitation of the previous approaches that largely rely on feature detection. Our experiments show that our method not only predicts features accurately, but also enriches the correspondences, which prolongs the track length of features.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s00371-020-01928-z | The Visual Computer |
Keywords | DocType | Volume |
Feature point prediction, Homography, Projective plane, Video sequence | Journal | 36 |
Issue | ISSN | Citations |
10 | 0178-2789 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huajun Liu | 1 | 6 | 2.79 |
| Shiran Tang | 2 | 0 | 0.34 |
| Dian Lei | 3 | 0 | 1.69 |
| Zhu Qing | 4 | 7 | 4.87 |
| Haigang Sui | 5 | 40 | 13.76 |
| Gaojian Zhang | 6 | 0 | 0.34 |
| Chao Li | 7 | 0 | 0.68 |