Deep Machine Learning for STEM Image Analysis Full article
| Journal |
Mendeleev Communications
ISSN: 0959-9436 , E-ISSN: 1364-551X |
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| Output data | Year: 2024, Volume: 34, Number: 6, Pages: 774-775 Pages count : 2 DOI: 10.1016/j.mencom.2024.10.002 | ||||
| Tags | deep machine learning; STEM; automatic recognition of objects; supported catalysts; neural network; microscopy; image analysis | ||||
| Authors |
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| Affiliations |
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Funding (1)
| 1 | Ministry of Science and Higher Education of the Russian Federation | FWUR-2024-0032 |
Abstract:
The universal, user-friendly online iOk Platform for automatic recognition of any type of objects in images based on deep machine learning is presented. Services aggregated in the iOk Platform significantly reduce the time spent on quantitative image analysis, decrease the influence of the subjective factor and increase the accuracy of the analysis by expanding the set of data that can be analyzed automatically. It is shown how the services can be used to analyze scanning transmission electron microscopy images obtained in heterogeneous catalysis studies, allowing for measurements of thousands of objects in an image, as well as simultaneous analysis of objects of different types, namely: nanoparticles and single sites.
Cite:
Nartova A.V.
, Matveev A.V.
, Kovtunova L.M.
, Okunev A.G.
Deep Machine Learning for STEM Image Analysis
Mendeleev Communications. 2024. V.34. N6. P.774-775. DOI: 10.1016/j.mencom.2024.10.002 WOS Scopus OpenAlex
Deep Machine Learning for STEM Image Analysis
Mendeleev Communications. 2024. V.34. N6. P.774-775. DOI: 10.1016/j.mencom.2024.10.002 WOS Scopus OpenAlex
Dates:
| Submitted: | May 23, 2024 |
| Published online: | Nov 29, 2024 |
| Published print: | Dec 1, 2024 |
Identifiers:
| Web of science: | WOS:001371818800001 |
| Scopus: | 2-s2.0-85210408896 |
| OpenAlex: | W4404841693 |
Citing:
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