Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/42902
Title: COVID-19 Control by Computer Vision Approaches: A Survey
Contributor(s): Ulhaq, Anwaar (author); Born, Jannis (author); Khan, Asim (author); Gomes, Douglas Pinto Sampaio (author); Chakraborty, Subrata  (author)orcid ; Paul, Manoranjan (author)
Publication Date: 2020-10-12
Early Online Version: 2020-09-29
Open Access: Yes
DOI: 10.1109/ACCESS.2020.3027685Open Access Link
Handle Link: https://hdl.handle.net/1959.11/42902
Abstract: 

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.

Publication Type: Journal Article
Source of Publication: IEEE Access, v.8, p. 179437-179456
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Place of Publication: United States of America
ISSN: 2169-3536
Fields of Research (FoR) 2020: 460102 Applications in health
461103 Deep learning
460308 Pattern recognition
Socio-Economic Objective (SEO) 2020: 209999 Other health not elsewhere classified
280115 Expanding knowledge in the information and computing sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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