Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Hans-Georg Luigs"'
Publikováno v:
Frontiers in Plant Science, Vol 13 (2022)
The beet cyst nematode Heterodera schachtii is a plant pest responsible for crop loss on a global scale. Here, we introduce a high-throughput system based on computer vision that allows quantifying beet cyst nematode infestation and measuring phenoty
Externí odkaz:
https://doaj.org/article/79611798e94b46628ae3c5b180a06c9f
Autor:
Anna Brugger, Jan Behmann, Stefan Paulus, Hans-Georg Luigs, Matheus Thomas Kuska, Patrick Schramowski, Kristian Kersting, Ulrike Steiner, Anne-Katrin Mahlein
Publikováno v:
Remote Sensing, Vol 11, Iss 12, p 1401 (2019)
Previous plant phenotyping studies have focused on the visible (VIS, 400−700 nm), near-infrared (NIR, 700−1000 nm) and short-wave infrared (SWIR, 1000−2500 nm) range. The ultraviolet range (UV, 200−380 nm) has not yet been used in plant pheno
Externí odkaz:
https://doaj.org/article/e8eb36855a494a748a4c650e219e410e
Autor:
Long, Chen, Martin, Strauch, Matthias, Daub, Hans-Georg, Luigs, Marcus, Jansen, Dorit, Merhof
Publikováno v:
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
Image segmentation models trained only with image-level labels have become increasingly popular as they require significantly less annotation effort than models trained with scribble, bounding box or pixel-wise annotations. While methods utilizing im
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3140455bc313c626d9310464b02e0ea
https://doi.org/10.1109/embc48229.2022.9871517
https://doi.org/10.1109/embc48229.2022.9871517
Autor:
Xiaoting Shao, Hans-Georg Luigs, Stefano Teso, Kristian Kersting, Wolfgang Stammer, Anna Brugger, Patrick Schramowski, Franziska Herbert, Anne-Katrin Mahlein
Publikováno v:
Nature Machine Intelligence
Deep neural networks have demonstrated excellent performances in many real-world applications. Unfortunately, they may show Clever Hans-like behaviour (making use of confounding factors within datasets) to achieve high performance. In this work we in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7792cd374c90511e4ebd393cf394e4ed
https://lirias.kuleuven.be/handle/123456789/669529
https://lirias.kuleuven.be/handle/123456789/669529
Autor:
Marcus Jansen, Martin Strauch, Xiaochen Jiang, Long Chen, Dorit Merhof, Hans-Georg Luigs, Matthias Daub, Stefan Krüssel, Susanne Schultz-Kuhlmann
Publikováno v:
ISBI
Plant parasitic nematodes cause damage to crop plants on a global scale. Robust detection on image data is a prerequisite for monitoring such nematodes, as well as for many biological studies involving the nematode C. elegans, a common model organism
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::61e42ad34cec53a138c93334f6e24d50
https://doi.org/10.1109/isbi45749.2020.9098465
https://doi.org/10.1109/isbi45749.2020.9098465
Publikováno v:
EMBC
Nematodes are plant parasites that cause damage to crops. In order to quantify nematode infestation based on soil samples, we propose an instance segmentation method that will serve as the basis of automatic quantitative analysis. We consider light m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c38289cf749f7e20401d0392473b6e9
https://pubmed.ncbi.nlm.nih.gov/31947199
https://pubmed.ncbi.nlm.nih.gov/31947199
Autor:
Daub, Matthias, Long Chen, Strauch, Martin, Jansen, Marcus, Hans-Georg, Luigs, Schultz-Kuhlmann, Susanne, Krüssel, Stefan, Merhof, Dorit
Publikováno v:
Julius-Kühn-Archiv; 2021, Issue 467, p217-218, 2p