PFSDS023
MINERALS: Machine learning training set for virtual microscopy
| Grant period | 12 months |
| Funding body | Exploratory Research Space der RWTH Aachen |
| ERS | |
| Identifier | G:(DE-82)EXS-PF-PFSDS023 |
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Journal Article
Superpixel segmentations for thin sections: Evaluation of methods to enable the generation of machine learning training data sets
Computers & geosciences 170, 105232 (2023) [10.1016/j.cageo.2022.105232]
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Preprint
Superpixel segmentations for thin sections: evaluation of methods to enable the generation of machine learning training data sets
42 Seiten : Illustrationen, Diagramme (2021) [10.31223/X55S65]
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Master Thesis
Knowledge driven machine learning image segmentation on thin section images
Aachen (2020) = Masterarbeit, Rheinisch-Westfälische Technische Hochschule Aachen, 2020
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Abstract/Contribution to a conference proceedings
Generating a pixel-wise annotated training dataset to train ML algorithms for mineral identification in rock thin sections
Abstracts & presentations / EGU General Assembly 2020
EGU General Assembly 2020, EGU 2020, onlineonline, 4 May 2020 - 8 May 2020
Göttingen : Copernicus Gesellschaft mbH EGU2020-18865, 1 Seite (2020) [10.5194/egusphere-egu2020-18865]
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