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pro vyhledávání: '"Bodesheim, Paul"'
Automatic camera-assisted monitoring of insects for abundance estimations is crucial to understand and counteract ongoing insect decline. In this paper, we present two datasets of nocturnal insects, especially moths as a subset of Lepidoptera, photog
Externí odkaz:
http://arxiv.org/abs/2307.15433
Biodiversity monitoring is crucial for tracking and counteracting adverse trends in population fluctuations. However, automatic recognition systems are rarely applied so far, and experts evaluate the generated data masses manually. Especially the sup
Externí odkaz:
http://arxiv.org/abs/2307.15427
Autor:
Körschens, Matthias, Bucher, Solveig Franziska, Bodesheim, Paul, Ulrich, Josephine, Denzler, Joachim, Römermann, Christine
Publikováno v:
In Ecological Informatics May 2024 80
Autor:
Körschens, Matthias, Bodesheim, Paul, Römermann, Christine, Bucher, Solveig Franziska, Migliavacca, Mirco, Ulrich, Josephine, Denzler, Joachim
Monitoring the responses of plants to environmental changes is essential for plant biodiversity research. This, however, is currently still being done manually by botanists in the field. This work is very laborious, and the data obtained is, though f
Externí odkaz:
http://arxiv.org/abs/2106.11154
Bias in classifiers is a severe issue of modern deep learning methods, especially for their application in safety- and security-critical areas. Often, the bias of a classifier is a direct consequence of a bias in the training dataset, frequently caus
Externí odkaz:
http://arxiv.org/abs/2103.06179
Part-based approaches for fine-grained recognition do not show the expected performance gain over global methods, although explicitly focusing on small details that are relevant for distinguishing highly similar classes. We assume that part-based met
Externí odkaz:
http://arxiv.org/abs/2007.02080
Fine-grained visual categorization is a classification task for distinguishing categories with high intra-class and small inter-class variance. While global approaches aim at using the whole image for performing the classification, part-based solutio
Externí odkaz:
http://arxiv.org/abs/1909.07075
Akademický článek
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Autor:
van Klink, Roel, August, Tom, Bas, Yves, Bodesheim, Paul, Bonn, Aletta, Fossøy, Frode, Høye, Toke T., Jongejans, Eelke, Menz, Myles H.M., Miraldo, Andreia, Roslin, Tomas, Roy, Helen E., Ruczyński, Ireneusz, Schigel, Dmitry, Schäffler, Livia, Sheard, Julie K., Svenningsen, Cecilie, Tschan, Georg F., Wäldchen, Jana, Zizka, Vera M.A., Åström, Jens, Bowler, Diana E.
Publikováno v:
In Trends in Ecology & Evolution October 2022 37(10):872-885
Autor:
Rodner, Erik, Barz, Björn, Guanche, Yanira, Flach, Milan, Mahecha, Miguel, Bodesheim, Paul, Reichstein, Markus, Denzler, Joachim
We present new methods for batch anomaly detection in multivariate time series. Our methods are based on maximizing the Kullback-Leibler divergence between the data distribution within and outside an interval of the time series. An empirical analysis
Externí odkaz:
http://arxiv.org/abs/1610.06761