Снижение погрешности микроскопических измерений сквозных отверстий за счет оптимизации коэффициента относительного смещения порога при адаптивной бинаризации изображения
Работая с сайтом, я даю свое согласие на использование файлов cookie. Это необходимо для нормального функционирования сайта, показа целевой рекламы и анализа трафика. Статистика использования сайта обрабатывается системой Яндекс.Метрика
Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Reducing of the microscope error for the control of through holes by optimizing the relative threshold offset coefficient with adaptive image binarization

Kovalev A.V.,  Bushuev S.V.,  Shulepov A.V. 

UDC 004.932:681.7
DOI: 10.26102/2310-6018/2026.61.10.001

  • Abstract
  • List of references
  • About authors

Video measuring microscopes (VMM) are widely used in scientific research and in production to control the geometry of objects of complex shape, however, the measurement results on them strongly depend on lighting conditions and image processing parameters. In this paper, the influence of the backlight level on the choice of the optimal value of the relative threshold offset coefficient relative to the local background for adaptive binarization of images when measuring through holes is investigated. This parameter has been optimized according to the criterion of minimizing the deviation of the measured diameter from the reference data obtained on the coordinate measuring machine (CMM, Carl Zeiss). After processing the images obtained from the microscope and calculating the parameters of the obtained circle profiles, it was shown that the optimal value of the relative threshold offset coefficient demonstrates a close to linear dependence on the level of the lower illumination (30–85 in conventional VMM units). Based on the data obtained, a method was proposed for automating the selection of the relative threshold offset coefficient, which reduces the operator's influence on the measurement results. The materials of the article can be used to increase reproducibility and reduce measurement error in industrial systems of automated optical geometry control of finished products.

1. Zuykov A.A., Shulepov A.V. Improving accuracy at measuring small-scale holes by means of reading microscope based on image digital processing. Electromechanical matters. VNIIEM studies. 2012;127(2):45–48. (In Russ.).

2. Shimizu Y., Chen L.-Ch., Kim D.W., et al. An insight into optical metrology in manufacturing. Measurement Science and Technology. 2021;32(4):042003. https://doi.org/10.1088/1361-6501/abc578

3. Makhesana M.A., Bagga P.J., Patel K.M., et al. Comparative analysis of different machine vision algorithms for tool wear measurement during machining. Journal of Intelligent Manufacturing. 2025;36(7):4567–4591. https://doi.org/10.1007/s10845-024-02467-3

4. Otsu N. A threshold selection method from gray-level histograms. Automatica. 1975;11:23–27.

5. Bradley D., Roth G. Adaptive thresholding using the integral image. Journal of Graphics Tools. 2007;12(2):13–21. https://doi.org/10.1080/2151237X.2007.10129236

6. Burger W., Burge M.J. Principles of Digital Image Processing: Core Algorithms. London: Springer; 2009. 332 p.

7. Canny J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1986;PAMI-8(6):679–698. https://doi.org/10.1109/TPAMI.1986.4767851

8. Illingworth J., Kittler J. The adaptive Hough transform. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1987;PAMI-9(5):690–698. https://doi.org/10.1109/TPAMI.1987.4767964

9. Montgomery D.C., Peck E.A., Vining G.G. Introduction to Linear Regression Analysis. Hoboken: John Wiley & Sons; 2022. 144 p.

10. Wang Y., Peng M., Cheng X. A subpixel localization method based on edge diffraction. In: Conference on Optical Design and Testing III, Beijing, China. SPIE; 2007. P. 607–610.

Kovalev Alexander Vyacheslavovich

eLibrary |

Moscow State University of Technology "STANKIN"

Moscow, Russian Federation

Bushuev Semyon Viktorovich

eLibrary |

Moscow State University of Technology "STANKIN"

Moscow, Russian Federation

Shulepov Alexey Vileninovich
Candidate of Engineering Sciences, Docent

eLibrary |

Moscow State University of Technology "STANKIN"

Moscow, Russian Federation

Keywords: video measuring microscope, measurement optimization, optical measurement errors, projection measurements, pattern recognition

For citation: Kovalev A.V., Bushuev S.V., Shulepov A.V. Reducing of the microscope error for the control of through holes by optimizing the relative threshold offset coefficient with adaptive image binarization. Modeling, Optimization and Information Technology. 2026;14(10). URL: https://moitvivt.ru/ru/journal/article?id=2386 DOI: 10.26102/2310-6018/2026.61.10.001 (In Russ).

© Kovalev A.V., Bushuev S.V., Shulepov A.V. Статья опубликована на условиях лицензии Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NS 4.0)
11

Full text in PDF

Скачать JATS XML

Received 17.06.2026

Revised 21.09.2026

Accepted 30.09.2026