pimiПриборы и методы измеренийDevices and Methods of Measurements2220-95062414-0473BNTU10.21122/2220-9506-2023-14-3-199-206pimi-832Research ArticleМетоды измерений, контроля, диагностикиMethods of measurements, monitoring, diagnosticsПрименение преобразования Хафа для контроля дисперсности накладывающихся частиц и их агломератовApplication of the Hough Transform to Dispersion Control of Overlapping Particles and Their AgglomeratesГуляевП. В.GulyaevP. V.

ул. имени Т. Барамзиной, 34, г. Ижевск 426067

T. Baramzina str., 34, Izhevsk 426067, Russia 

lucac@inbox.ruУдмуртский федеральный исследовательский центр Уральского отделения Российской академии наукРоссияUdmurt Federal Research Center of the Ural Branch of the Russian Academy of SciencesRussian Federation202306102023143199206Copyright © Гуляев П.В., 20232023Гуляев П.В.Gulyaev P.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://pimi.bntu.by/jour/article/view/832

Контроль дисперсности микро- и наночастиц по изображениям имеет большое значение для обеспечения заданных свойств самих частиц и материалов на их основе. Целью данной работы являлось исследование возможностей применения преобразования Хафа для контроля дисперсности накладывающихся частиц и их агломератов. Анализ применения преобразования Хафа для накладывающихся частиц и их агломератов показал следующее. Особенности конвенциональной реализации приводят к предпочтительной регистрации больших частиц, смещению центров перекрывающихся частиц, искажению размеров. Для корректного использования преобразования Хафа требуется точная настройка всех его параметров. Для автоматизации такой настройки исследованы зависимости количества и размера регистрируемых на изображении частиц от параметров преобразования Хафа. Исследования проводились на тестовых изображениях с известным количеством и размерами частиц. Результаты показали, что при изменении пороговых параметров преобразования Хафа число регистрируемых частиц стабилизируется вблизи их оптимальных значений. При изменении диапазона размеров, регистрируемых преобразованием Хафа частиц изменяется гистограмма распределения частиц по размерам. При этом оптимальная ширина диапазона определяется по наиболее устойчивым экстремумам гистограммы. Максимальное межцентровое расстояние устанавливается не менее половины оптимального диапазона. Описан и реализован алгоритм настройки, подразумевающий многократный запуск преобразования Хафа с различными комбинациями параметров. Алгоритм включает этапы грубой и точной настройки, позволяющие точнее приблизится к оптимальным параметрам. Работоспособность алгоритма подтверждена на тестовых и реальных изображениях. При этом погрешности определения размеров и количества частиц многопроходового преобразования Хафа находятся на одном уровне или превосходят данные показатели у методов-аналогов.

The dispersion control of micro- and nanoparticles by their images is of great importance for ensuring the specified properties of the particles themselves and materials based on them. The aim of this article was to consider the possibilities of using the Hough transform for dispersion control of overlapping particles and their agglomerates. Analysis of the application of the Hough transform for overlapping particles and their agglomerates showed the following. The particularities of the conventional implementation lead to the preferred registration of large particles, the shift of the centers of overlapping particles, and the distortion of the size values. To use the Hough transform correctly, fine-tuning of all its parameters is required. To automate this process, the dependences of the number and size of particles recorded in the image on the parameters of the Hough transform was investigated. The studies were carried out on test images with a known number and size of particles. The results showed that when the threshold parameters of the Hough transform change, the number of detected particles stabilizes near their optimal values. When the size range of particles detected by the Hough transform changes, the histogram of the particle size distribution changes. In this case, the optimal width of the range is determined by the most stable extremes of the histogram. The maximum center-to-center distance is set at least half of the optimal range. The configuration algorithm is described and implemented. It implies repeatedly running the Hough transform with different combinations of parameters. The algorithm includes stages of coarse and fine-tuning, which allows to getting closer to the optimal parameters. The efficiency of the algorithm has been confirmed on test and real images. Tests have shown that the errors in determining the size and number of particles of the multi-pass Hough transform are on the same level or exceed these indicators for analog methods.

сканирующий зондовый микроскопСЗМ-изображениечастицыконтроль дисперсностиscanning probe microscopeSPM imageparticlesdispersion controlReferencesISO 13322-1:2014. Particle size analysis – Image analysis methods. Part 1: Static image analysis methods. 2nd ed.; Publisher: International Organization for standardization, Switzerland, 2014.ISO 13322-1:2014. Particle size analysis – Image analysis methods. Part 1: Static image analysis methods. 2nd ed.; Publisher: International Organization for standardization, Switzerland, 2014.Chaki N, Shaikh SH, Saeed K. A Comprehensive Survey on Image Binarization Techniques. Exploring Image Binarization Techniques. Studies in Computational Intelligence. Springer, New Delhi. 2014;560:5-15. DOI: 10.1007/978-81-322-1907-1_2Chaki N, Shaikh SH, Saeed K. A Comprehensive Survey on Image Binarization Techniques. Exploring Image Binarization Techniques. Studies in Computational Intelligence. Springer, New Delhi. 2014;560:5-15. DOI: 10.1007/978-81-322-1907-1_2Sezgin BS. A survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging. 2004;13(1):146-165. DOI: 10.1117/1.1631315Sezgin BS. A survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging. 2004;13(1):146-165. DOI: 10.1117/1.1631315Ramadevi Y, Sridevi T, Poornima B, Kalyani B. Segmentation and Object Recognition Using Edge Detection Techniques. International Journal of Computer Science and Information Technology. 2010;2:153-161. DOI: 10.5121/IJCSIT.2010.2614Ramadevi Y, Sridevi T, Poornima B, Kalyani B. Segmentation and Object Recognition Using Edge Detection Techniques. International Journal of Computer Science and Information Technology. 2010;2:153-161. DOI: 10.5121/IJCSIT.2010.2614Navon E, Miller O, Averbuch A. Color image segmentation based on adaptive local thresholds. Image and Vision Computing. 2005;23(1):69-85. DOI: 10.1016/j.imavis.2004.05.011Navon E, Miller O, Averbuch A. Color image segmentation based on adaptive local thresholds. Image and Vision Computing. 2005;23(1):69-85. DOI: 10.1016/j.imavis.2004.05.011Bui K, Fauman J, Kes D, et. al. Segmentation of scanning tunneling microscopy images using variational methods and empirical wavelets. Pattern Analysis and Applications. 2020;23:625-651. DOI: 10.1007/s10044-019-00824-0Bui K, Fauman J, Kes D, et. al. Segmentation of scanning tunneling microscopy images using variational methods and empirical wavelets. Pattern Analysis and Applications. 2020;23:625-651. DOI: 10.1007/s10044-019-00824-0Ronneberger O, Fischer Ph, Brox T. U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Lecture Notes in Computer Science. Springer, Cham. 2015;9351:234-241. DOI: 10.1007/978-3-319-24574-4_28Ronneberger O, Fischer Ph, Brox T. U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Lecture Notes in Computer Science. Springer, Cham. 2015;9351:234-241. DOI: 10.1007/978-3-319-24574-4_28Pătrăucean V, Gurdjos P, von Gioi RG. A Parameterless Line Segment and Elliptical Arc Detector with Enhanced Ellipse Fitting. Computer Vision – ECCV 2012. Lecture Notes in Computer Science, Springer, Berlin. 2012:7573:572-585. DOI: 10.1007/978-3-642-33709-3_41Pătrăucean V, Gurdjos P, von Gioi RG. A Parameterless Line Segment and Elliptical Arc Detector with Enhanced Ellipse Fitting. Computer Vision – ECCV 2012. Lecture Notes in Computer Science, Springer, Berlin. 2012:7573:572-585. DOI: 10.1007/978-3-642-33709-3_41Gulyaev PV. Measurement of the length of objects on scanning probe microscope images using curvature detectors. Measurement Techniques. 2021;64(1):21-27. DOI: 10.1007/s11018-021-01890-9Gulyaev PV. Measurement of the length of objects on scanning probe microscope images using curvature detectors. Measurement Techniques. 2021;64(1):21-27. DOI: 10.1007/s11018-021-01890-9Gulyaev PV, Shelkovnikov EYu, Tyurikov AV. Peculiarities of surface curvature detectors usage for nanoparticles size analysis. Chemical Physics and Mesoscopy. 2013;15(1):138-143. (In Russ.).Gulyaev PV, Shelkovnikov EYu, Tyurikov AV. Peculiarities of surface curvature detectors usage for nanoparticles size analysis. Chemical Physics and Mesoscopy. 2013;15(1):138-143. (In Russ.).MountainsSPIP. [Electronic Resource]. Available at: https://www.digitalsurf.com/software-solutions/scanning-probe-microscopy/ [Accessed 07.05.2023].MountainsSPIP. [Electronic Resource]. Available at: https://www.digitalsurf.com/software-solutions/scanning-probe-microscopy/ [Accessed 07.05.2023].OpenCV. [Electronic Resource]. Available at: https://opencv.org/ [Accessed 07.05.2023].OpenCV. [Electronic Resource]. Available at: https://opencv.org/ [Accessed 07.05.2023].Zhelebovskiy AA, Sumin AA, Dmitrichenkov NV. Application of the Hough algorithm for sorting polydisperse microparticles. Proceedings of XVI International Scientific and Technical Conference, June 28–July 02, 2021. Moscow: Publishing House "Pero". 2021;80-85: 260 p.Zhelebovskiy AA, Sumin AA, Dmitrichenkov NV. Application of the Hough algorithm for sorting polydisperse microparticles. Proceedings of XVI International Scientific and Technical Conference, June 28–July 02, 2021. Moscow: Publishing House "Pero". 2021;80-85: 260 p.Lebedev SA, Ososkov GA. Fast algorithms for ring recognition and electron identification in the CBM RICH detector. Physics of particles and nuclei letters. 2009;6(2):161-176. DOI: 10.1134/S1547477109020095Lebedev SA, Ososkov GA. Fast algorithms for ring recognition and electron identification in the CBM RICH detector. Physics of particles and nuclei letters. 2009;6(2):161-176. DOI: 10.1134/S1547477109020095Atherton TJ, Kerbyson DJ. Size invariant circle detection. Image and Vision Computing. 1999;17(11): 795-803. DOI: 10.1016/S0262-8856(98)00160-7Atherton TJ, Kerbyson DJ. Size invariant circle detection. Image and Vision Computing. 1999;17(11): 795-803. DOI: 10.1016/S0262-8856(98)00160-7Okunev AG, Mashukov MY, Nartova AV, Matveev AV. Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning. Nanomaterials. 2020;10(7)(1285). DOI: 10.3390/nano10071285Okunev AG, Mashukov MY, Nartova AV, Matveev AV. Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning. Nanomaterials. 2020;10(7)(1285). DOI: 10.3390/nano10071285

The authors declare that there are no conflicts of interest present.