Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Using Computer Vision and Deep Learning for Nanoparticle Recognition on Scanning Probe Microscopy Images : Modified U-net Approach. / Liz, Mikhail F.; Nartova, Anna V.; Matveev, Andrey V. и др.
Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020. Institute of Electrical and Electronics Engineers Inc., 2020. стр. 13-16 9303184 (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Using Computer Vision and Deep Learning for Nanoparticle Recognition on Scanning Probe Microscopy Images
T2 - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
AU - Liz, Mikhail F.
AU - Nartova, Anna V.
AU - Matveev, Andrey V.
AU - Okunev, Aleksey G.
N1 - Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/11/14
Y1 - 2020/11/14
N2 - Particles characterization is a significant part of numerous studies in material sciences and engineering technologies. Microscopy images of materials containing particles are usually analyzed by operator with manual counting and measuring of particle sizing by a software ruler. Traditional automated image analyzing methods such as edge detection, segmentation, etc. are not universal, giving poor results on noisy pictures and need empirical fitted parameters. To realize automatic method of particles recognition on scanning tunneling microscopy (STM) data we used U-net and modified U-net neural networks, which was trained on ten STM images contained 1918 particles. Verification on 3 pictures with 695 particles showed mAP=0.12 for modified U-net neural network.
AB - Particles characterization is a significant part of numerous studies in material sciences and engineering technologies. Microscopy images of materials containing particles are usually analyzed by operator with manual counting and measuring of particle sizing by a software ruler. Traditional automated image analyzing methods such as edge detection, segmentation, etc. are not universal, giving poor results on noisy pictures and need empirical fitted parameters. To realize automatic method of particles recognition on scanning tunneling microscopy (STM) data we used U-net and modified U-net neural networks, which was trained on ten STM images contained 1918 particles. Verification on 3 pictures with 695 particles showed mAP=0.12 for modified U-net neural network.
KW - deep neural networks
KW - particles recognition
KW - scanning probe microscopy
UR - http://www.scopus.com/inward/record.url?scp=85099564872&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/5ace8e43-886a-3306-81d8-6735d769cea0/
U2 - 10.1109/S.A.I.ence50533.2020.9303184
DO - 10.1109/S.A.I.ence50533.2020.9303184
M3 - Conference contribution
AN - SCOPUS:85099564872
SN - 9780738131115
T3 - Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
SP - 13
EP - 16
BT - Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 November 2020 through 15 November 2020
ER -
ID: 27607280