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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...

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Detalles Bibliográficos
Autores principales: Miyashita, Leo, Nakamura, Akihiro, Odagawa, Takuto, Ishikawa, Masatoshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8156490/
https://www.ncbi.nlm.nih.gov/pubmed/34065726
http://dx.doi.org/10.3390/s21103469
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author Miyashita, Leo
Nakamura, Akihiro
Odagawa, Takuto
Ishikawa, Masatoshi
author_facet Miyashita, Leo
Nakamura, Akihiro
Odagawa, Takuto
Ishikawa, Masatoshi
author_sort Miyashita, Leo
collection PubMed
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 [Formula: see text] 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.
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spelling pubmed-81564902021-05-28 BIFNOM: Binary-Coded Features on Normal Maps Miyashita, Leo Nakamura, Akihiro Odagawa, Takuto Ishikawa, Masatoshi Sensors (Basel) Article 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 [Formula: see text] 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. MDPI 2021-05-16 /pmc/articles/PMC8156490/ /pubmed/34065726 http://dx.doi.org/10.3390/s21103469 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Miyashita, Leo
Nakamura, Akihiro
Odagawa, Takuto
Ishikawa, Masatoshi
BIFNOM: Binary-Coded Features on Normal Maps
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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8156490/
https://www.ncbi.nlm.nih.gov/pubmed/34065726
http://dx.doi.org/10.3390/s21103469
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