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0575308 - ÚPT 2024 GB eng A - Abstract
Čermák, Jan - Ambrož, Ondřej - Zouhar, Martin - Jozefovič, Patrik - Mikmeková, Šárka
Methodology for Collecting and Aligning Correlative SEM, CLSM and LOM Images of Bulk Material Microstructure to Create a Large Machine Learning Training Dataset.
Microscopy and Microanalysis. Cambridge University Press. Roč. 29, S1 (2023), s. 2016-2018. ISSN 1431-9276. E-ISSN 1435-8115.
[Microscopy & Microanalysis 2023. 23.07.2023-27.07.2023, Minneapolis]
R&D Projects: GA TA ČR(CZ) TN02000020
Grant - others:AV ČR(CZ) LQ100652201
Program: Prémie Lumina quaeruntur
Institutional support: RVO:68081731
Keywords : correlative microscopy * metalography * specimen navigation * image registration * machine learning dataset
OECD category: Materials engineering
https://academic.oup.com/mam/article/29/Supplement_1/2016/7228070
Permanent Link: https://hdl.handle.net/11104/0345091