Standard

Recognition of nanoparticles on scanning probe microscopy images using computer vision and deep machine learning. / Okunev, Aleksey G.; Nartova, Anna V.; Matveev, Andrey V.

SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 940-943 8958363 (SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Okunev, AG, Nartova, AV & Matveev, AV 2019, Recognition of nanoparticles on scanning probe microscopy images using computer vision and deep machine learning. in SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings., 8958363, SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 940-943, 2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019, Novosibirsk, Russian Federation, 21.10.2019. https://doi.org/10.1109/SIBIRCON48586.2019.8958363

APA

Okunev, A. G., Nartova, A. V., & Matveev, A. V. (2019). Recognition of nanoparticles on scanning probe microscopy images using computer vision and deep machine learning. In SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings (pp. 940-943). [8958363] (SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIBIRCON48586.2019.8958363

Vancouver

Okunev AG, Nartova AV, Matveev AV. Recognition of nanoparticles on scanning probe microscopy images using computer vision and deep machine learning. In SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 940-943. 8958363. (SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings). doi: 10.1109/SIBIRCON48586.2019.8958363

Author

Okunev, Aleksey G. ; Nartova, Anna V. ; Matveev, Andrey V. / Recognition of nanoparticles on scanning probe microscopy images using computer vision and deep machine learning. SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 940-943 (SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings).

BibTeX

@inproceedings{6118504946dc40cb98d680fc739bdc86,
title = "Recognition of nanoparticles on scanning probe microscopy images using computer vision and deep machine learning",
abstract = "Identifying and counting individual particles is an important component of many studies in various explorations. In the paper we present the results of the application of deep learning methods for the automated recognition of platinum nanoparticles deposited on highly oriented pyrolytic graphite (HOPG) on images obtained by scanning tunneling microscopy (STM). We used the neural network CascadeRCNN. The training was performed on a data set containing 10 STM images with 1918 nanoparticles. Five images containing 2052 nanoparticles were used for verification. As a result, the trained neural network recognized nanoparticles in verification set with 50.8% accuracy. Nanoparticles are specified as distinct contours, which are necessary for further determination of the particles dimensions (size, height etc). The obtained results were compared with the possibilities of other software products. The advantage of using deep machine learning methods for automatic particle recognition is clearly shown.",
keywords = "deep neural networks, nanoparticles, particles recognition, scanning tunneling microscopy",
author = "Okunev, {Aleksey G.} and Nartova, {Anna V.} and Matveev, {Andrey V.}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019 ; Conference date: 21-10-2019 Through 27-10-2019",
year = "2019",
month = oct,
doi = "10.1109/SIBIRCON48586.2019.8958363",
language = "English",
series = "SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "940--943",
booktitle = "SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings",
address = "United States",

}

RIS

TY - GEN

T1 - Recognition of nanoparticles on scanning probe microscopy images using computer vision and deep machine learning

AU - Okunev, Aleksey G.

AU - Nartova, Anna V.

AU - Matveev, Andrey V.

N1 - Publisher Copyright: © 2019 IEEE.

PY - 2019/10

Y1 - 2019/10

N2 - Identifying and counting individual particles is an important component of many studies in various explorations. In the paper we present the results of the application of deep learning methods for the automated recognition of platinum nanoparticles deposited on highly oriented pyrolytic graphite (HOPG) on images obtained by scanning tunneling microscopy (STM). We used the neural network CascadeRCNN. The training was performed on a data set containing 10 STM images with 1918 nanoparticles. Five images containing 2052 nanoparticles were used for verification. As a result, the trained neural network recognized nanoparticles in verification set with 50.8% accuracy. Nanoparticles are specified as distinct contours, which are necessary for further determination of the particles dimensions (size, height etc). The obtained results were compared with the possibilities of other software products. The advantage of using deep machine learning methods for automatic particle recognition is clearly shown.

AB - Identifying and counting individual particles is an important component of many studies in various explorations. In the paper we present the results of the application of deep learning methods for the automated recognition of platinum nanoparticles deposited on highly oriented pyrolytic graphite (HOPG) on images obtained by scanning tunneling microscopy (STM). We used the neural network CascadeRCNN. The training was performed on a data set containing 10 STM images with 1918 nanoparticles. Five images containing 2052 nanoparticles were used for verification. As a result, the trained neural network recognized nanoparticles in verification set with 50.8% accuracy. Nanoparticles are specified as distinct contours, which are necessary for further determination of the particles dimensions (size, height etc). The obtained results were compared with the possibilities of other software products. The advantage of using deep machine learning methods for automatic particle recognition is clearly shown.

KW - deep neural networks

KW - nanoparticles

KW - particles recognition

KW - scanning tunneling microscopy

UR - http://www.scopus.com/inward/record.url?scp=85079065198&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/ed160936-7f32-3948-b616-ef1f46e33694/

U2 - 10.1109/SIBIRCON48586.2019.8958363

DO - 10.1109/SIBIRCON48586.2019.8958363

M3 - Conference contribution

T3 - SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings

SP - 940

EP - 943

BT - SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019

Y2 - 21 October 2019 through 27 October 2019

ER -

ID: 23427305