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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...

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Detalles Bibliográficos
Autores principales: Li, Zhenghao, Yang, Junying, Zhao, Jiaduo, Han, Peng, Chai, Zhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
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.
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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
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