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Adaptive Hough Transform Circle Detection Application in Developed 2D Coordinate Measuring System

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper describes a cheap, portable coordinate system for measuring circular geometries. The developed software can measure diameters, circle center coordinates, and distances between them. The circle detector uses the Adaptive Hough Transform implemented in the OpenCV library. For image rectification, the system uses reference circle positions on the plane parallel to the plane of the measured ones. Pixel size is determined by the distances between the reference circle positions on the same plane. The mean measurement value from a series of photos was found efficient estimation of accurate value, significantly improving overall system accuracy.
Twórcy
  • Laboratory of Coordinate Metrology, Faculty of Mechanical Engineering, Cracow University of Technology, Al. Jana Pawła II 37, 31-864 Kraków, Poland
autor
  • Laboratory of Coordinate Metrology, Faculty of Mechanical Engineering, Cracow University of Technology, Al. Jana Pawła II 37, 31-864 Kraków, Poland
  • Laboratory of Coordinate Metrology, Faculty of Mechanical Engineering, Cracow University of Technology, Al. Jana Pawła II 37, 31-864 Kraków, Poland
  • Laboratory of Coordinate Metrology, Faculty of Mechanical Engineering, Cracow University of Technology, Al. Jana Pawła II 37, 31-864 Kraków, Poland
Bibliografia
  • 1. Xiang R., He W., Zhang X., Wang D., Shan Y. Size measurement based on a two-camera machine vision system for the bayonets of automobile brake pads. Measurement 2018; 122: 106–16.
  • 2. Sładek J., Ostrowska K., Kohut P., Holak K., Gąska A., Uhl T. Development of a vision based deflection measurement system and its accuracy assessment. Measurement 2013; 46(3): 1237–49.
  • 3. Gadelmawla E.S. Computer vision algorithms for measurement and inspection of spur gears. Measurement 2011; 44(9): 1669–78.
  • 4. Peng G., Zhang Z., Li W. Computer vision algorithm for measurement and inspection of O-rings. Measurement 2016; 94: 828–36.
  • 5. Illingworth J., Kittler J. The Adaptive Hough Transform. IEEE Trans Pattern Anal Mach Intell. 1987; PAMI-9(5): 690–8.
  • 6. Yuen H.K., Princen J., Illingworth J., Kittler J.. Comparative study of Hough Transform methods for circle finding. Image Vis Comput 1990 1; 8(1): 71–7.
  • 7. OpenCV: Feature Detection [Internet]. [cited 2023 Jul 6]. Available from: https://docs.opencv. org/4.6.0/dd/d1a/group__imgproc__feature.html# ga47849c3be0d0406ad3ca45db65a25d2d
  • 8. Wang Y., Cheng G. Application of gradient-based Hough transform to the detection of corrosion pits in optical images. Appl Surf Sci. 2016; 366: 9–18.
  • 9. opencv/doc/pattern.png at master · opencv/opencv [Internet]. GitHub. [cited 2023 Aug 7]. Available from: https://github.com/opencv/opencv/blob/master/doc/pattern.png
  • 10. OpenCV: Camera Calibration and 3D Reconstruction [Internet]. [cited 2023 Aug 8]. Available from: https://docs.opencv.org/4.8.0/d9/d0c/group__cali- b3d.html#ga4abc2ece9fab9398f2e560d53c8c9780
  • 11. OpenCV: Geometric Image Transformations [Internet]. [cited 2023 Aug 8]. Available from: https:// docs.opencv.org/4.8.0/da/d54/group__imgproc__ transform.html#gaf73673a7e8e18ec6963e3774e6 a94b87
  • 12. ISO 10360-7: 2011. Geneva, Switzerland: International Organization for Standardization
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a16deaf1-b811-400c-bcad-af150c739b98
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