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PIMR: Parallel and Integrated Matching for Raw Data
With the trend of high-resolution imaging, computational costs of image matching have substantially increased. In order to find the compromise between accuracy and computation in real-time applications, we bring forward a fast and robust matching algorithm, named parallel and integrated matching for...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4732087/ https://www.ncbi.nlm.nih.gov/pubmed/26729132 http://dx.doi.org/10.3390/s16010054 |
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author | Li, Zhenghao Yang, Junying Zhao, Jiaduo Han, Peng Chai, Zhi |
author_facet | Li, Zhenghao Yang, Junying Zhao, Jiaduo Han, Peng Chai, Zhi |
author_sort | Li, Zhenghao |
collection | PubMed |
description | With the trend of high-resolution imaging, computational costs of image matching have substantially increased. In order to find the compromise between accuracy and computation in real-time applications, we bring forward a fast and robust matching algorithm, named parallel and integrated matching for raw data (PIMR). This algorithm not only effectively utilizes the color information of raw data, but also designs a parallel and integrated framework to shorten the time-cost in the demosaicing stage. Experiments show that compared to existing state-of-the-art methods, the proposed algorithm yields a comparable recognition rate, while the total time-cost of imaging and matching is significantly reduced. |
format | Online Article Text |
id | pubmed-4732087 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-47320872016-02-12 PIMR: Parallel and Integrated Matching for Raw Data Li, Zhenghao Yang, Junying Zhao, Jiaduo Han, Peng Chai, Zhi Sensors (Basel) Article With the trend of high-resolution imaging, computational costs of image matching have substantially increased. In order to find the compromise between accuracy and computation in real-time applications, we bring forward a fast and robust matching algorithm, named parallel and integrated matching for raw data (PIMR). This algorithm not only effectively utilizes the color information of raw data, but also designs a parallel and integrated framework to shorten the time-cost in the demosaicing stage. Experiments show that compared to existing state-of-the-art methods, the proposed algorithm yields a comparable recognition rate, while the total time-cost of imaging and matching is significantly reduced. MDPI 2016-01-02 /pmc/articles/PMC4732087/ /pubmed/26729132 http://dx.doi.org/10.3390/s16010054 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Zhenghao Yang, Junying Zhao, Jiaduo Han, Peng Chai, Zhi PIMR: Parallel and Integrated Matching for Raw Data |
title | PIMR: Parallel and Integrated Matching for Raw Data |
title_full | PIMR: Parallel and Integrated Matching for Raw Data |
title_fullStr | PIMR: Parallel and Integrated Matching for Raw Data |
title_full_unstemmed | PIMR: Parallel and Integrated Matching for Raw Data |
title_short | PIMR: Parallel and Integrated Matching for Raw Data |
title_sort | pimr: parallel and integrated matching for raw data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4732087/ https://www.ncbi.nlm.nih.gov/pubmed/26729132 http://dx.doi.org/10.3390/s16010054 |
work_keys_str_mv | AT lizhenghao pimrparallelandintegratedmatchingforrawdata AT yangjunying pimrparallelandintegratedmatchingforrawdata AT zhaojiaduo pimrparallelandintegratedmatchingforrawdata AT hanpeng pimrparallelandintegratedmatchingforrawdata AT chaizhi pimrparallelandintegratedmatchingforrawdata |