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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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. |
format | Online Article Text |
id | pubmed-6263410 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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