Deep Machine Learning for STEM Image Analysis | Sciact - CRIS-система Института катализа

Deep Machine Learning for STEM Image Analysis Научная публикация

Журнал

Mendeleev Communications
ISSN: 0959-9436 , E-ISSN: 1364-551X

Вых. Данные

Год: 2024, Том: 34, Номер: 6, Страницы: 774-775 Страниц : 2 DOI: 10.1016/j.mencom.2024.10.002

Ключевые слова

deep machine learning; STEM; automatic recognition of objects; supported catalysts; neural network; microscopy; image analysis

Авторы

Nartova Anna V. ^1,2 , Matveev Andrey V. ¹ , Kovtunova Larisa M. ^1,2 , Okunev Aleksey G. ¹

Организации

1	Department of Chemistry, Novosibirsk State University, 630090 Novosibirsk, Russian Federation
2	G. K. Boreskov Institute of Catalysis, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russian Federation

Информация о финансировании (1)

Министерство науки и высшего образования Российской Федерации (с 15 мая 2018)

FWUR-2024-0032

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.

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

Поступила в редакцию:	23 мая 2024 г.
Опубликована online:	29 нояб. 2024 г.
Опубликована в печати:	1 дек. 2024 г.

Web of science:	WOS:001371818800001
Scopus:	2-s2.0-85210408896
OpenAlex:	W4404841693

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