Abstract
As a typical feature point with the distinct advantage of being detected easily, the circle has been widely used for camera calibration and motion measurement. However, motion blur may cause a negative effect on the accuracy of the center location. In this paper, the developed method for the circle detection on motion blur image is proposed, which consists of two procedures. Wiener filtering is used to restore a degraded image in the first step. Zernike moment is utilized to subpixel central location in the second step. Image restoring simulation and center detection experiments are provided to verify the performance of the method. Results show that the clarity of the images restored by Weiner filtering is high and the circles on the restored image can be detected successfully and located accurately.
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References
Pinto T, Kohler C, Albertazzi A. Regular mesh measurement of large free form surfaces using stereo vision and fringe projection. Opt Lasers Eng. 2012;50(7):910–6.
Aguilar JJ, Torres F, Lope MA. Stereo vision for 3D measurement: accuracy analysis, calibration and industrial applications. Measurement. 1996;18(4):193–200.
Zhou XG, Nai guang L, Deng WY et al. Image point correspondence using coded targets. J Beijing Inst Mach. 2002.
Guo SY, Zhang XF, Zhang F. Adaptive randomized hough transform for circle detection using moving window. In: International conference on machine learning and cybernetics. IEEE; 2006. p. 3880–5.
Kim HS, Kim JH, et al. A two-step circle detection algorithm from the intersecting chords. Pattern Recogn Lett. 2001;22(6–7):787–98.
Deng ZF, Xiong YL. Identification of motion-blur direction based on frequency-domain algorithm. Opto-Electron Eng. 2007;34(10):98–101.
Schneider C-T. 3D Vermessung von Oberflächen und Bauteilen durch Photogrammetrie und Bildverarbeitung. In: Proceedings of the IDENT/VISION’91, May 14–17; Stuttgart, Germany; 1991. p. 90–3.
Moghaddam ME, Jamzad M. Finding point spread function of motion blur using Radon transform and modeling the motion length. In: IEEE international symposium on signal processing and information technology. IEEE; 2004. p. 314–17.
Zhou Y. Study on Wiener filtering for restoration of motion blurred image. Comput Eng Appl. 2009;45(19):181–3.
Chen Q. Identification the scale of the point spread function from the motion blurred image. Comput Eng Appl. 2004;40(23):15–7.
Ghosal S, Mehrotra R. Orthogonal moment operators for subpixel edge detection. Pattern Recogn. 1993;26(2):295–306.
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Liu, F., Zhou, X., Huo, J., Liu, Y., Yang, M., Liu, S. (2020). A Novel Method for Detecting the Circle on Motion-Blurred Image. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_27
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DOI: https://doi.org/10.1007/978-981-13-6504-1_27
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