Unknown

Dataset Information

0

BIFNOM: Binary-Coded Features on Normal Maps.


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.

SUBMITTER: Miyashita L 

PROVIDER: S-EPMC8156490 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC8385964 | biostudies-literature
| S-EPMC3511067 | biostudies-literature
| S-EPMC5752089 | biostudies-other
| S-EPMC3176264 | biostudies-literature
| S-EPMC7174771 | biostudies-literature
| S-EPMC6773416 | biostudies-literature
| S-EPMC5952606 | biostudies-literature
| S-EPMC6216044 | biostudies-literature
| S-EPMC4759574 | biostudies-literature
| S-EPMC3137217 | biostudies-literature