{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:59:43Z","timestamp":1760234383395,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,16]],"date-time":"2021-05-16T00:00:00Z","timestamp":1621123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s21103469","type":"journal-article","created":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T02:31:34Z","timestamp":1621218694000},"page":"3469","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["BIFNOM: Binary-Coded Features on Normal Maps"],"prefix":"10.3390","volume":"21","author":[{"given":"Leo","family":"Miyashita","sequence":"first","affiliation":[{"name":"Data Science Research Division, Information Technology Center, The University of Tokyo, Tokyo 133-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akihiro","family":"Nakamura","sequence":"additional","affiliation":[{"name":"Data Science Research Division, Information Technology Center, The University of Tokyo, Tokyo 133-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takuto","family":"Odagawa","sequence":"additional","affiliation":[{"name":"Data Science Research Division, Information Technology Center, The University of Tokyo, Tokyo 133-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masatoshi","family":"Ishikawa","sequence":"additional","affiliation":[{"name":"Data Science Research Division, Information Technology Center, The University of Tokyo, Tokyo 133-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1109\/TSMC.1977.4309663","article-title":"Coarse-Fine Template Matching","volume":"7","author":"Rosenfeld","year":"1977","journal-title":"IEEE Trans. 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