Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
iOk Platform for Automated Object Detection and Analysis in Microscopy Images. / Kudinov, Vitalii Yu.; Matveev, Andrey V.; Nartova, Anna V. et al.
Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023. Institute of Electrical and Electronics Engineers (IEEE), 2023. p. 1420-1423.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - iOk Platform for Automated Object Detection and Analysis in Microscopy Images
AU - Kudinov, Vitalii Yu.
AU - Matveev, Andrey V.
AU - Nartova, Anna V.
AU - Sankova, Natalya N.
AU - Belotserkovskii, Valerii A.
AU - Okunev, Aleksey G.
N1 - Conference code: 16
PY - 2023
Y1 - 2023
N2 - Counting, measuring, and identifying particles is a crucial aspect of various research endeavors. Typically, images containing particles are manually processed using a software ruler. Automated processing techniques, which rely on conventional image processing methods such as edge detection and segmentation, are not universally applicable and require setting several parameters through trial and error. Additionally, these techniques can only be utilized on high-quality images. Also, the ambiguity of the data set can greatly affect the quality of object identification. The report presents the iOk platform (iok.nsu.ru), which uses artificial intelligence through the ParticlesNN web service and Telegram bots DLgram and No Code ML as well as other means of detecting objects on an image. The platform provides automatic search and analysis of objects in images without pre-processing, regardless of the type and quality of the image. At the output, you can obtain information about object recognition, its area and size, as well as its position in the image. The neural network can be trained on user images, no programming skills are required.
AB - Counting, measuring, and identifying particles is a crucial aspect of various research endeavors. Typically, images containing particles are manually processed using a software ruler. Automated processing techniques, which rely on conventional image processing methods such as edge detection and segmentation, are not universally applicable and require setting several parameters through trial and error. Additionally, these techniques can only be utilized on high-quality images. Also, the ambiguity of the data set can greatly affect the quality of object identification. The report presents the iOk platform (iok.nsu.ru), which uses artificial intelligence through the ParticlesNN web service and Telegram bots DLgram and No Code ML as well as other means of detecting objects on an image. The platform provides automatic search and analysis of objects in images without pre-processing, regardless of the type and quality of the image. At the output, you can obtain information about object recognition, its area and size, as well as its position in the image. The neural network can be trained on user images, no programming skills are required.
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85182274888&origin=inward&txGid=b1410bafffdfddc5e1e28afed299f280
UR - https://www.mendeley.com/catalogue/23bb72fe-a03a-3a98-a2a8-031ef8e7bf08/
U2 - 10.1109/apeie59731.2023.10347794
DO - 10.1109/apeie59731.2023.10347794
M3 - Conference contribution
SN - 9798350330885
SP - 1420
EP - 1423
BT - Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering
Y2 - 10 November 2023 through 12 November 2023
ER -
ID: 59613744