COVID-19 Control by Computer Vision Approaches: A Survey
The COVID-19 pandemic has triggered an urgent call to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of artificial intelligence, has enjoyed recent success in solving various complex problems in health care and has the potential to contribut...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9208758/ |
| _version_ | 1818988492594610176 |
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| author | Anwaar Ulhaq Jannis Born Asim Khan Douglas Pinto Sampaio Gomes Subrata Chakraborty Manoranjan Paul |
| author_facet | Anwaar Ulhaq Jannis Born Asim Khan Douglas Pinto Sampaio Gomes Subrata Chakraborty Manoranjan Paul |
| author_sort | Anwaar Ulhaq |
| collection | DOAJ |
| description | The COVID-19 pandemic has triggered an urgent call to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of artificial intelligence, has enjoyed recent success in solving various complex problems in health care and has the potential to contribute to the fight of controlling COVID-19. In response to this call, computer vision researchers are putting their knowledge base at test to devise effective ways to counter COVID-19 challenge and serve the global community. New contributions are being shared with every passing day. It motivated us to review the recent work, collect information about available research resources, and an indication of future research directions. We want to make it possible for computer vision researchers to find existing and future research directions. This survey article presents a preliminary review of the literature on research community efforts against COVID-19 pandemic. |
| first_indexed | 2024-12-20T19:23:27Z |
| format | Article |
| id | doaj.art-f4014aa50e414b7d90a4873d2741178c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T19:23:27Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f4014aa50e414b7d90a4873d2741178c2022-12-21T19:28:56ZengIEEEIEEE Access2169-35362020-01-01817943717945610.1109/ACCESS.2020.30276859208758COVID-19 Control by Computer Vision Approaches: A SurveyAnwaar Ulhaq0https://orcid.org/0000-0002-5145-7276Jannis Born1https://orcid.org/0000-0001-8307-5670Asim Khan2https://orcid.org/0000-0003-0543-3350Douglas Pinto Sampaio Gomes3https://orcid.org/0000-0003-2610-0678Subrata Chakraborty4https://orcid.org/0000-0002-0102-5424Manoranjan Paul5https://orcid.org/0000-0001-6870-5056School of Computing and Mathematics, Charles Sturt University, Port Macquarie, NSW, AustraliaDepartment for Biosystems Science and Engineering, ETH Zurich, Basel, SwitzerlandCollege of Engineering and Science, Victoria University, Melbourne, VIC, AustraliaCollege of Engineering and Science, Victoria University, Melbourne, VIC, AustraliaFaculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, AustraliaSchool of Computing and Mathematics, Charles Sturt University, Port Macquarie, NSW, AustraliaThe COVID-19 pandemic has triggered an urgent call to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of artificial intelligence, has enjoyed recent success in solving various complex problems in health care and has the potential to contribute to the fight of controlling COVID-19. In response to this call, computer vision researchers are putting their knowledge base at test to devise effective ways to counter COVID-19 challenge and serve the global community. New contributions are being shared with every passing day. It motivated us to review the recent work, collect information about available research resources, and an indication of future research directions. We want to make it possible for computer vision researchers to find existing and future research directions. This survey article presents a preliminary review of the literature on research community efforts against COVID-19 pandemic.https://ieeexplore.ieee.org/document/9208758/Artificial intelligenceCOVID-19computer visionreviewsurvey |
| spellingShingle | Anwaar Ulhaq Jannis Born Asim Khan Douglas Pinto Sampaio Gomes Subrata Chakraborty Manoranjan Paul COVID-19 Control by Computer Vision Approaches: A Survey IEEE Access Artificial intelligence COVID-19 computer vision review survey |
| title | COVID-19 Control by Computer Vision Approaches: A Survey |
| title_full | COVID-19 Control by Computer Vision Approaches: A Survey |
| title_fullStr | COVID-19 Control by Computer Vision Approaches: A Survey |
| title_full_unstemmed | COVID-19 Control by Computer Vision Approaches: A Survey |
| title_short | COVID-19 Control by Computer Vision Approaches: A Survey |
| title_sort | covid 19 control by computer vision approaches a survey |
| topic | Artificial intelligence COVID-19 computer vision review survey |
| url | https://ieeexplore.ieee.org/document/9208758/ |
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