Name
Abstract | ||
|---|---|---|
We propose a novel method for detecting features on normal maps and describing binary features, called BIFNOM, which is three-dimensionally rotation invariant and detects and matches interest points at high speed regardless of whether a target is textured or textureless and rigid or non-rigid. Conventional methods of detecting features on normal maps can also be applied to textureless targets, in contrast with features on luminance images; however, they cannot deal with three-dimensional rotation between each pair of corresponding interest points due to the definition of orientation, or they have difficulty achieving fast detection and matching due to a heavy-weight descriptor. We addressed these issues by introducing a three dimensional local coordinate system and converting a normal vector to a binary code, and achieved more than 750fps real-time feature detection and matching. Furthermore, we present an extended descriptor and criteria for real-time tracking, and evaluate the performance with both simulation and actual system. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21103469 | SENSORS |
Keywords | DocType | Volume |
surface normal, feature point, binary, real-time tracking | Journal | 21 |
Issue | ISSN | Citations |
10 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leo Miyashita | 1 | 23 | 7.13 |
| Akihiro Nakamura | 2 | 0 | 0.34 |
| Takuto Odagawa | 3 | 0 | 0.34 |
| Masatoshi Ishikawa | 4 | 0 | 0.34 |