Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Simon-Martin Schröder"'
Autor:
Simon-Martin Schröder, Rainer Kiko
Publikováno v:
Sensors, Vol 22, Iss 7, p 2775 (2022)
Image annotation is a time-consuming and costly task. Previously, we published MorphoCluster as a novel image annotation tool to address problems of conventional, classifier-based image annotation approaches: their limited efficiency, training set bi
Externí odkaz:
https://doaj.org/article/1b1c33c340c94eaba45e6ff004c1dec5
Autor:
Lars Schmarje, Johannes Brünger, Monty Santarossa, Simon-Martin Schröder, Rainer Kiko, Reinhard Koch
Publikováno v:
Sensors, Vol 21, Iss 19, p 6661 (2021)
Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large and consistently labeled datasets. Although current approaches in sem
Externí odkaz:
https://doaj.org/article/f46ca07e0a63421bb7a7eec7f858b666
Publikováno v:
Sensors, Vol 20, Iss 11, p 3060 (2020)
In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue t
Externí odkaz:
https://doaj.org/article/a50ff817ecdd41898201aaa9731d8938
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
instname
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
instname
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
https://hal.univ-lorraine.fr/hal-03482282/document
Publikováno v:
Sensors (Basel, Switzerland)
Sensors, Vol 20, Iss 3060, p 3060 (2020)
Sensors
Volume 20
Issue 11
Sensors, Vol 20, Iss 3060, p 3060 (2020)
Sensors
Volume 20
Issue 11
In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f1fa39f92662b37c73f9bbe159c9d448
http://europepmc.org/articles/PMC7308937
http://europepmc.org/articles/PMC7308937
Publikováno v:
Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science
Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science, 11269, 2019, 978-3-030-12939-2
Lecture Notes in Computer Science ISBN: 9783030129385
GCPR
Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science, 11269, 2019, 978-3-030-12939-2
Lecture Notes in Computer Science ISBN: 9783030129385
GCPR
International audience; The size of current plankton image datasets renders manual classification virtually infeasible. The training of models for machine classification is complicated by the fact that a large number of classes consist of only a few
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b0af6da1ee3ea0c69a5b880fb12a17d
https://hal.archives-ouvertes.fr/hal-03390570
https://hal.archives-ouvertes.fr/hal-03390570
Autor:
Florian Schütte, Lars Stemmann, Bernd Christiansen, Simon-Martin Schröder, Helena Hauss, Peter Brandt, Henk-Jan T. Hoving, Reinhard Koch, Johannes Karstensen, Marc Picheral, Arne Körtzinger, Rainer Kiko, Bruce H Robison, Svenja Christiansen
Publikováno v:
Limnology and Oceanography, 63 (5). p. 2109.
Limnology and Oceanography Bulletin
Limnology and Oceanography Bulletin, American Society of Limnology and Oceanography, 2018, 63 (5), pp.2093-2109. ⟨10.1002/lno.10926⟩
Limnology and Oceanography, 63 (5). pp. 2093-2109.
Limnology and Oceanography Bulletin
Limnology and Oceanography Bulletin, American Society of Limnology and Oceanography, 2018, 63 (5), pp.2093-2109. ⟨10.1002/lno.10926⟩
Limnology and Oceanography, 63 (5). pp. 2093-2109.
International audience; Gelatinous zooplankton hold key functions in the ocean and have been shown to significantly influence the transport of organic carbon to the deep sea. We discovered a gelatinous, flux-feeding polychaete of the genus Poeobius i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92ba79048fc394b43a2d3006128b37fa
http://oceanrep.geomar.de/43671/
http://oceanrep.geomar.de/43671/
Publikováno v:
IEEE Access, Vol 9, Pp 82146-82168 (2021)
While deep learning strategies achieve outstanding results in computer vision tasks, one issue remains: The current strategies rely heavily on a huge amount of labeled data. In many real-world problems, it is not feasible to create such an amount of
Externí odkaz:
https://doaj.org/article/19eacb9697b84a03ab19befee1c03787