Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/42902
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dc.contributor.authorUlhaq, Anwaaren
dc.contributor.authorBorn, Jannisen
dc.contributor.authorKhan, Asimen
dc.contributor.authorGomes, Douglas Pinto Sampaioen
dc.contributor.authorChakraborty, Subrataen
dc.contributor.authorPaul, Manoranjanen
dc.date.accessioned2022-02-21T00:26:13Z-
dc.date.available2022-02-21T00:26:13Z-
dc.date.issued2020-10-12-
dc.identifier.citationIEEE Access, v.8, p. 179437-179456en
dc.identifier.issn2169-3536en
dc.identifier.urihttps://hdl.handle.net/1959.11/42902-
dc.description.abstract<p>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.</p>en
dc.languageenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartofIEEE Accessen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleCOVID-19 Control by Computer Vision Approaches: A Surveyen
dc.typeJournal Articleen
dc.identifier.doi10.1109/ACCESS.2020.3027685en
dc.identifier.pmid34812357en
dcterms.accessRightsGolden
local.contributor.firstnameAnwaaren
local.contributor.firstnameJannisen
local.contributor.firstnameAsimen
local.contributor.firstnameDouglas Pinto Sampaioen
local.contributor.firstnameSubrataen
local.contributor.firstnameManoranjanen
local.profile.schoolSchool of Science and Technologyen
local.profile.emailschakra3@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeUnited States of Americaen
local.format.startpage179437en
local.format.endpage179456en
local.identifier.scopusid85099078228en
local.peerreviewedYesen
local.identifier.volume8en
local.title.subtitleA Surveyen
local.access.fulltextYesen
local.contributor.lastnameUlhaqen
local.contributor.lastnameBornen
local.contributor.lastnameKhanen
local.contributor.lastnameGomesen
local.contributor.lastnameChakrabortyen
local.contributor.lastnamePaulen
dc.identifier.staffune-id:schakra3en
local.profile.orcid0000-0002-0102-5424en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/42902en
local.date.onlineversion2020-09-29-
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleCOVID-19 Control by Computer Vision Approachesen
local.relation.fundingsourcenoteThis work was supported by Charles Sturt University, COVID-19 Fund.en
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorUlhaq, Anwaaren
local.search.authorBorn, Jannisen
local.search.authorKhan, Asimen
local.search.authorGomes, Douglas Pinto Sampaioen
local.search.authorChakraborty, Subrataen
local.search.authorPaul, Manoranjanen
local.open.fileurlhttps://rune.une.edu.au/web/retrieve/23cdb11c-ba06-4fa3-bb0f-521a83961949en
local.uneassociationNoen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.available2020en
local.year.published2020en
local.fileurl.openhttps://rune.une.edu.au/web/retrieve/23cdb11c-ba06-4fa3-bb0f-521a83961949en
local.fileurl.openpublishedhttps://rune.une.edu.au/web/retrieve/23cdb11c-ba06-4fa3-bb0f-521a83961949en
local.subject.for2020460102 Applications in healthen
local.subject.for2020461103 Deep learningen
local.subject.for2020460308 Pattern recognitionen
local.subject.seo2020209999 Other health not elsewhere classifieden
local.subject.seo2020280115 Expanding knowledge in the information and computing sciencesen
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School of Science and Technology
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