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Perspectives on Individual Animal Identification from Biology and Computer Vision
Identifying individual animals is crucial for many biological investigations. In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. Here, we review current advances of computer vision identification te...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490693/ https://www.ncbi.nlm.nih.gov/pubmed/34050741 http://dx.doi.org/10.1093/icb/icab107 |
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author | Vidal, Maxime Wolf, Nathan Rosenberg, Beth Harris, Bradley P Mathis, Alexander |
author_facet | Vidal, Maxime Wolf, Nathan Rosenberg, Beth Harris, Bradley P Mathis, Alexander |
author_sort | Vidal, Maxime |
collection | PubMed |
description | Identifying individual animals is crucial for many biological investigations. In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. Here, we review current advances of computer vision identification techniques to provide both computer scientists and biologists with an overview of the available tools and discuss their applications. We conclude by offering recommendations for starting an animal identification project, illustrate current limitations, and propose how they might be addressed in the future. |
format | Online Article Text |
id | pubmed-8490693 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-84906932021-10-05 Perspectives on Individual Animal Identification from Biology and Computer Vision Vidal, Maxime Wolf, Nathan Rosenberg, Beth Harris, Bradley P Mathis, Alexander Integr Comp Biol Symposium Identifying individual animals is crucial for many biological investigations. In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. Here, we review current advances of computer vision identification techniques to provide both computer scientists and biologists with an overview of the available tools and discuss their applications. We conclude by offering recommendations for starting an animal identification project, illustrate current limitations, and propose how they might be addressed in the future. Oxford University Press 2021-05-29 /pmc/articles/PMC8490693/ /pubmed/34050741 http://dx.doi.org/10.1093/icb/icab107 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Symposium Vidal, Maxime Wolf, Nathan Rosenberg, Beth Harris, Bradley P Mathis, Alexander Perspectives on Individual Animal Identification from Biology and Computer Vision |
title | Perspectives on Individual Animal Identification from Biology and Computer Vision |
title_full | Perspectives on Individual Animal Identification from Biology and Computer Vision |
title_fullStr | Perspectives on Individual Animal Identification from Biology and Computer Vision |
title_full_unstemmed | Perspectives on Individual Animal Identification from Biology and Computer Vision |
title_short | Perspectives on Individual Animal Identification from Biology and Computer Vision |
title_sort | perspectives on individual animal identification from biology and computer vision |
topic | Symposium |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490693/ https://www.ncbi.nlm.nih.gov/pubmed/34050741 http://dx.doi.org/10.1093/icb/icab107 |
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