BIFNOM: Binary-Coded Features on Normal Maps
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. Conve...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/10/3469 |
| _version_ | 1797533919985270784 |
|---|---|
| author | Leo Miyashita Akihiro Nakamura Takuto Odagawa Masatoshi Ishikawa |
| author_facet | Leo Miyashita Akihiro Nakamura Takuto Odagawa Masatoshi Ishikawa |
| author_sort | Leo Miyashita |
| collection | DOAJ |
| 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>750</mn><mspace width="0.222222em"></mspace><mi>fps</mi></mrow></semantics></math></inline-formula> 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. |
| first_indexed | 2024-03-10T11:22:30Z |
| format | Article |
| id | doaj.art-f214a71b4a48492990d511f1e542e4fb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:22:30Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f214a71b4a48492990d511f1e542e4fb2023-11-21T19:58:30ZengMDPI AGSensors1424-82202021-05-012110346910.3390/s21103469BIFNOM: Binary-Coded Features on Normal MapsLeo Miyashita0Akihiro Nakamura1Takuto Odagawa2Masatoshi Ishikawa3Data Science Research Division, Information Technology Center, The University of Tokyo, Tokyo 133-8656, JapanData Science Research Division, Information Technology Center, The University of Tokyo, Tokyo 133-8656, JapanData Science Research Division, Information Technology Center, The University of Tokyo, Tokyo 133-8656, JapanData Science Research Division, Information Technology Center, The University of Tokyo, Tokyo 133-8656, JapanWe 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>750</mn><mspace width="0.222222em"></mspace><mi>fps</mi></mrow></semantics></math></inline-formula> 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.https://www.mdpi.com/1424-8220/21/10/3469surface normalfeature pointbinaryreal-time tracking |
| spellingShingle | Leo Miyashita Akihiro Nakamura Takuto Odagawa Masatoshi Ishikawa BIFNOM: Binary-Coded Features on Normal Maps Sensors surface normal feature point binary real-time tracking |
| title | BIFNOM: Binary-Coded Features on Normal Maps |
| title_full | BIFNOM: Binary-Coded Features on Normal Maps |
| title_fullStr | BIFNOM: Binary-Coded Features on Normal Maps |
| title_full_unstemmed | BIFNOM: Binary-Coded Features on Normal Maps |
| title_short | BIFNOM: Binary-Coded Features on Normal Maps |
| title_sort | bifnom binary coded features on normal maps |
| topic | surface normal feature point binary real-time tracking |
| url | https://www.mdpi.com/1424-8220/21/10/3469 |
| work_keys_str_mv | AT leomiyashita bifnombinarycodedfeaturesonnormalmaps AT akihironakamura bifnombinarycodedfeaturesonnormalmaps AT takutoodagawa bifnombinarycodedfeaturesonnormalmaps AT masatoshiishikawa bifnombinarycodedfeaturesonnormalmaps |