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Retinomorphic optoelectronic devices for intelligent machine vision
Biological visual system can efficiently handle optical information within the retina and visual cortex of the brain, which suggests an alternative approach for the upgrading of the current low-intelligence, large energy consumption, and complex circuitry of the artificial vision system for high-per...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8762475/ https://www.ncbi.nlm.nih.gov/pubmed/35072015 http://dx.doi.org/10.1016/j.isci.2021.103729 |
_version_ | 1784633771320934400 |
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author | Chen, Weilin Zhang, Zhang Liu, Gang |
author_facet | Chen, Weilin Zhang, Zhang Liu, Gang |
author_sort | Chen, Weilin |
collection | PubMed |
description | Biological visual system can efficiently handle optical information within the retina and visual cortex of the brain, which suggests an alternative approach for the upgrading of the current low-intelligence, large energy consumption, and complex circuitry of the artificial vision system for high-performance edge computing applications. In recent years, retinomorphic machine vision based on the integration of optoelectronic image sensors and processors has been regarded as a promising candidate to improve this phenomenon. This novel intelligent machine vision technology can perform information preprocessing near or even within the sensor in the front end, thereby reducing the transmission of redundant raw data and improving the efficiency of the back-end processor for high-level computing tasks. In this contribution, we try to present a comprehensive review on the recent progress achieved in this emergent field. |
format | Online Article Text |
id | pubmed-8762475 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-87624752022-01-20 Retinomorphic optoelectronic devices for intelligent machine vision Chen, Weilin Zhang, Zhang Liu, Gang iScience Review Biological visual system can efficiently handle optical information within the retina and visual cortex of the brain, which suggests an alternative approach for the upgrading of the current low-intelligence, large energy consumption, and complex circuitry of the artificial vision system for high-performance edge computing applications. In recent years, retinomorphic machine vision based on the integration of optoelectronic image sensors and processors has been regarded as a promising candidate to improve this phenomenon. This novel intelligent machine vision technology can perform information preprocessing near or even within the sensor in the front end, thereby reducing the transmission of redundant raw data and improving the efficiency of the back-end processor for high-level computing tasks. In this contribution, we try to present a comprehensive review on the recent progress achieved in this emergent field. Elsevier 2022-01-01 /pmc/articles/PMC8762475/ /pubmed/35072015 http://dx.doi.org/10.1016/j.isci.2021.103729 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Chen, Weilin Zhang, Zhang Liu, Gang Retinomorphic optoelectronic devices for intelligent machine vision |
title | Retinomorphic optoelectronic devices for intelligent machine vision |
title_full | Retinomorphic optoelectronic devices for intelligent machine vision |
title_fullStr | Retinomorphic optoelectronic devices for intelligent machine vision |
title_full_unstemmed | Retinomorphic optoelectronic devices for intelligent machine vision |
title_short | Retinomorphic optoelectronic devices for intelligent machine vision |
title_sort | retinomorphic optoelectronic devices for intelligent machine vision |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8762475/ https://www.ncbi.nlm.nih.gov/pubmed/35072015 http://dx.doi.org/10.1016/j.isci.2021.103729 |
work_keys_str_mv | AT chenweilin retinomorphicoptoelectronicdevicesforintelligentmachinevision AT zhangzhang retinomorphicoptoelectronicdevicesforintelligentmachinevision AT liugang retinomorphicoptoelectronicdevicesforintelligentmachinevision |