Machine learning techniques to characterize functional traits of plankton from image data

Autor: Eric C. Orenstein, Sakina‐Dorothée Ayata, Frédéric Maps, Érica C. Becker, Fabio Benedetti, Tristan Biard, Thibault de Garidel‐Thoron, Jeffrey S. Ellen, Filippo Ferrario, Sarah L. C. Giering, Tamar Guy‐Haim, Laura Hoebeke, Morten Hvitfeldt Iversen, Thomas Kiørboe, Jean‐François Lalonde, Arancha Lana, Martin Laviale, Fabien Lombard, Tom Lorimer, Séverine Martini, Albin Meyer, Klas Ove Möller, Barbara Niehoff, Mark D. Ohman, Cédric Pradalier, Jean‐Baptiste Romagnan, Simon‐Martin Schröder, Virginie Sonnet, Heidi M. Sosik, Lars S. Stemmann, Michiel Stock, Tuba Terbiyik‐Kurt, Nerea Valcárcel‐Pérez, Laure Vilgrain, Guillaume Wacquet, Anya M. Waite, Jean‐Olivier Irisson

Publikováno v: Limnology and Oceanography
Limnology and Oceanography, 2022, 67 (8), pp.1647-1669. ⟨10.1002/lno.12101⟩
Orenstein, E C, Ayata, SD, Maps, F, Becker, É C, Benedetti, F, Biard, T, de Garidel-Thoron, T, Ellen, J S, Ferrario, F, Giering, S L C, Guy-Haim, T, Hoebeke, L, Iversen, M H, Kiørboe, T, Lalonde, JF, Lana, A, Laviale, M, Lombard, F, Lorimer, T, Martini, S, Meyer, A, Möller, K O, Niehoff, B, Ohman, M D, Pradalier, C, Romagnan, JB, Schröder, SM, Sonnet, V, Sosik, H M, Stemmann, L S, Stock, M, Terbiyik-Kurt, T, Valcárcel-Pérez, N, Vilgrain, L, Wacquet, G, Waite, A M & Irisson, JO 2022, ' Machine learning techniques to characterize functional traits of plankton from image data ', Limnology and Oceanography, vol. 67, no. 8, pp. 1647-1669 . https://doi.org/10.1002/lno.12101
Limnology And Oceanography (0024-3590) (Wiley), 2022-08, Vol. 67, N. 8, P. 1647-1669
LIMNOLOGY AND OCEANOGRAPHY
e-IEO. Repositorio Institucional Digital de Acceso Abierto del Instituto Español de Oceanografía
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Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ too

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b310387659399d19153332aae277f2ee
https://hal.univ-lorraine.fr/hal-03482282/document

Automated recognition of people and identification of animal species in camera trap images

Autor: Laura Hoebeke, Michiel Stock, Stijn Van Hoey, Jim Casaer, Bernard De Baets

Publikováno v: Research Institute for Nature and Forest
Hoebeke, L, Stock, M, Van Hoey, S, Casaer, J & De Baets, B 2018, ' Automated recognition of people and identification of animal species in camera trap images ' pp. 22 .

Camera traps are increasingly being used in wildlife monitoring. The great advantage of camera traps in comparison with other sampling methods is that very accurate data can be collected without the animal being collared or tagged nor the researcher

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::92244106aad1324b780ebde8556cd9fc
https://pureportal.inbo.be/portal/en/publications/automated-recognition-of-people-and-identification-of-animal-species-in-camera-trap-images(f123e441-611a-4a04-a8c3-e5eb459eeab1).html