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BIFNOM: Binary-Coded Features on Normal Maps
| Content Provider | MDPI |
|---|---|
| Author | Miyashita, Leo Nakamura, Akihiro Odagawa, Takuto Ishikawa, Masatoshi |
| Copyright Year | 2021 |
| Description | 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 |
| Starting Page | 3469 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21103469 |
| Journal | Sensors |
| Issue Number | 10 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-05-16 |
| Access Restriction | Open |
| Subject Keyword | Sensors Artificial Intelligence Surface Normal Feature Point Binary Real-time Tracking |
| Content Type | Text |
| Resource Type | Article |
