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A FAST-BRISK Feature Detector with Depth Information

RGB-D cameras offer both color and depth images of the surrounding environment, making them an attractive option for robotic and vision applications. This work introduces the BRISK_D algorithm, which efficiently combines Features from Accelerated Segment Test (FAST) and Binary Robust Invariant Scala...

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Detalles Bibliográficos
Autores principales: Liu, Yanli, Zhang, Heng, Guo, Hanlei, Xiong, Neal N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263410/
https://www.ncbi.nlm.nih.gov/pubmed/30428580
http://dx.doi.org/10.3390/s18113908
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author Liu, Yanli
Zhang, Heng
Guo, Hanlei
Xiong, Neal N.
author_facet Liu, Yanli
Zhang, Heng
Guo, Hanlei
Xiong, Neal N.
author_sort Liu, Yanli
collection PubMed
description RGB-D cameras offer both color and depth images of the surrounding environment, making them an attractive option for robotic and vision applications. This work introduces the BRISK_D algorithm, which efficiently combines Features from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Keypoints (BRISK) methods. In the BRISK_D algorithm, the keypoints are detected by the FAST algorithm and the location of the keypoint is refined in the scale and the space. The scale factor of the keypoint is directly computed with the depth information of the image. In the experiment, we have made a detailed comparative analysis of the three algorithms SURF, BRISK and BRISK_D from the aspects of scaling, rotation, perspective and blur. The BRISK_D algorithm combines depth information and has good algorithm performance.
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spelling pubmed-62634102018-12-12 A FAST-BRISK Feature Detector with Depth Information Liu, Yanli Zhang, Heng Guo, Hanlei Xiong, Neal N. Sensors (Basel) Article RGB-D cameras offer both color and depth images of the surrounding environment, making them an attractive option for robotic and vision applications. This work introduces the BRISK_D algorithm, which efficiently combines Features from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Keypoints (BRISK) methods. In the BRISK_D algorithm, the keypoints are detected by the FAST algorithm and the location of the keypoint is refined in the scale and the space. The scale factor of the keypoint is directly computed with the depth information of the image. In the experiment, we have made a detailed comparative analysis of the three algorithms SURF, BRISK and BRISK_D from the aspects of scaling, rotation, perspective and blur. The BRISK_D algorithm combines depth information and has good algorithm performance. MDPI 2018-11-13 /pmc/articles/PMC6263410/ /pubmed/30428580 http://dx.doi.org/10.3390/s18113908 Text en © 2018 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Yanli
Zhang, Heng
Guo, Hanlei
Xiong, Neal N.
A FAST-BRISK Feature Detector with Depth Information
title A FAST-BRISK Feature Detector with Depth Information
title_full A FAST-BRISK Feature Detector with Depth Information
title_fullStr A FAST-BRISK Feature Detector with Depth Information
title_full_unstemmed A FAST-BRISK Feature Detector with Depth Information
title_short A FAST-BRISK Feature Detector with Depth Information
title_sort fast-brisk feature detector with depth information
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263410/
https://www.ncbi.nlm.nih.gov/pubmed/30428580
http://dx.doi.org/10.3390/s18113908
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