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A real-time and precise ellipse detector via edge screening and aggregation

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Abstract

A fast and precise method for ellipse detection is proposed in this paper. The method aims at clearly removing the lines and curves which are not ellipse edges to improve the ellipse fitting. In arc extraction, the arcs are divided into four categories according to the gradient, and the size constraint is exploited to remove the interference lines. Then, the arc relative position constraints and the tangent lines constraint are employed to exactly group the arcs that belong to the same ellipse into a set. Finally, a post-processing approach is developed to remove the invalid ellipses. Due to the effective removal of the interference edges and the designed geometric multi-constraint, the computational costs of arc grouping and parameter estimation are dramatically reduced, and the fitting results are finely agreeable to the actual ellipse contours. The performance is evaluated with 3600 synthetic images and 1517 real images, and the experimental results demonstrate that the proposed method runs much faster than the current speed leading methods with the comparable or higher F-measure.

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References

  1. Li, W., Shan, S., Liu, H.: High-precision method of binocular camera calibration with a distortion model. Appl. Optics 56(8), 2368–2377 (2017)

    Article  Google Scholar 

  2. Da, F., Li, Q., Zhang, H., Fang, X.: Self-calibration using two same circles. Opt. Laser Technol. 44(6), 1924–1933 (2012)

    Article  Google Scholar 

  3. Liu, C., Hu, W.: Ellipse fitting for imaged cross sections of a surface of revolution. Pattern Recognit. 48(4), 1440–1454 (2015)

    Article  MATH  Google Scholar 

  4. Wang, Z., Wu, Z., Zhen, X., Yang, R., Xi, J., Chen, X.: A two-step calibration method of a large FOV binocular stereovision sensor for onsite measurement. Measurement 62(2), 15–24 (2015)

    Article  Google Scholar 

  5. Wang, Z., Wu, Z., Zhen, X., Yang, R., Xi, J., Chen, X.: An onsite inspection sensor for the formation of hull plates based on active binocular stereovision. Proc. IMechE Part B: J. Eng. Manuf. 492(3), 887–896 (2014)

    Google Scholar 

  6. Zafari, S., Eerola, T., Sampo, J., Kalviainen, H., Haario, H.: Segmentation of overlapping elliptical objects in silhouette images. IEEE Trans. Image Process. 24(12), 5942–5952 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  7. Duda, R.O., Hart, P.E.: Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 15(1), 11–15 (1972)

    Article  MATH  Google Scholar 

  8. Helgason, S.: The radon transform, vol. 2. Birkhäuser, Boston (1999)

    Book  MATH  Google Scholar 

  9. Ballard, D.H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognit. 13(2), 111–122 (1981)

    Article  MATH  Google Scholar 

  10. McLaughlin, R.A.: Randomized Hough transform: improved ellipse detection with comparison. Pattern Recognit. Lett. 19(3), 299–305 (1998)

    Article  MATH  Google Scholar 

  11. Kiryati, N., Eldar, Y., Bruckstein, A.M.: A probabilistic Hough transform. Pattern Recognit. 24(4), 303–316 (1991)

    Article  MathSciNet  Google Scholar 

  12. Kiryati, N., Kalviainen, H., Alaoutinen, S.: Randomized or probabilistic Hough transform: unified performance evaluation. Pattern Recognit. Lett. 21(13), 1157–1164 (2000)

    Article  MATH  Google Scholar 

  13. Lu, W., Tan, J.: Detection of incomplete ellipse in images with strong noise by iterative randomized Hough transform (IRHT). Pattern Recognit. 41(4), 1268–1279 (2008)

    Article  MATH  Google Scholar 

  14. Chia, A.Y., Rahardja, S., Rajan, D., Leung, M.K.: A split and merge based ellipse detector with self-correcting capability. IEEE Trans. Image Process. 20(7), 1991–2006 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  15. Lu, T., Hu, W., Liu, C., Yang, D.: Effective ellipse detector with polygonal curve and likelihood ratio test. Comput. Vis. IET 9(6), 914–925 (2015)

    Article  Google Scholar 

  16. Chen, S., Xia, R., Zhao, J., Chen, Y., Hu, M.: A hybrid method for ellipse detection in industrial images. Pattern Recognit. 68(18), 82–98 (2017)

    Article  Google Scholar 

  17. Fitzgibbon A.W., Pilu M., Fisher R.B.: Direct least square fitting of ellipses. In: Presented at ICPR (1996). http://ieeexplore.ieee.org/document/546029/

  18. Prasad, D.K., Leung, M.K.H., Quek, C.: ElliFit: an unconstrained, non-iterative, least squares based geometric ellipse fitting method. Pattern Recognit. 46(5), 1449–1465 (2013)

    Article  MATH  Google Scholar 

  19. Liang, J., Wang, Y., Zeng, X.: Robust ellipse fitting via half-quadratic and semidefinite relaxation optimization. IEEE Trans. Image Process. 24(11), 4276–4286 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  20. Mulleti, S., Seelamantula, C.S.: Ellipse fitting using the finite rate of innovation sampling principle. IEEE Trans. Image Process. 25(3), 1451–1464 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  21. Liu, D., Wang, Y., Tang, Z., Lu, X.: A robust circle detection algorithm based on top-down least-square fitting analysis. Comput. Electr. Eng. 40(4), 1415–1428 (2014)

    Article  Google Scholar 

  22. Wang Y., He Z., Liu X., Tang Z., Li L.: A fast and robust ellipse detector based on top-down least-square fitting. In: Presented at BMVC (2015). http://www.bmva.org/bmvc/2015/papers/paper156/abstract156.pdf

  23. Prasad, D.K., Leung, M.K., Cho, S.Y.: Edge curvature and convexity based ellipse detection method. Pattern Recognit. 45(9), 3204–3221 (2012)

    Article  Google Scholar 

  24. Fornaciari, M., Prati, A., Cucchiara, R.: A fast and effective ellipse detector for embedded vision applications. Pattern Recognit. 47(11), 3693–3708 (2014)

    Article  Google Scholar 

  25. Jia, Q., Fan, X., Luo, Z., Song, L., Qiu, T.: A Fast ellipse detector using projective invariant pruning. IEEE Trans. Image Process. 26(8), 3665–3679 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  26. Jin, R., Owais, H.M., Lin, D., Song, T., Yuan, Y.: Ellipse proposal and convolutional neural network discriminant for autonomous landing marker detection. J. Field Robot. 36(1), 6–16 (2019)

    Article  Google Scholar 

  27. Dong, H., Prasad, D.K., Chen, I.: Accurate detection of ellipses with false detection control at video rates using a gradient analysis. Pattern Recognit. 81, 112–130 (2018)

    Article  Google Scholar 

  28. Pătrăucean V., Gurdjos P., Gioi R.G.V.: A parameterless line segment and elliptical arc detector with enhanced ellipse fitting. In: Presented at ECCV (2012). https://link.springer.com/chapter/10.1007/978-3-642-33709-3_41

  29. Pătrăucean, V., Gurdjos, P., Gioi, R.G.V.: Joint A contrario ellipse and line detection. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 788–802 (2017)

    Article  Google Scholar 

  30. Liu Y., Xie Z., Wang B., Liu H., Jing Z., Huang C.: A practical detection of non-cooperative satellite based on ellipse fitting. In: Presented at ICMA (2016). http://ieeexplore.ieee.org/document/7558793/

  31. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986). https://doi.org/10.1109/TPAMI.1986.4767851

    Article  Google Scholar 

  32. Freeman, H.: Shapira R: determining the minimum-area encasing rectangle for an arbitrary closed curve. Commun. ACM 18(7), 409–413 (1975)

    Article  MATH  Google Scholar 

  33. Dorrie, B., Bydavidantin, T.: 100 Great problems of elementary mathematics, p. 264. Dover, New York (1965)

    Google Scholar 

  34. Prasad, D.K., Leung, M.K., Quek, C., Brown, M.S.: DEB: definite error bounded tangent estimator for digital curves. IEEE Trans. Image Process. 23(10), 4297–4310 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  35. Jaccard, P.: The distribution of flora in the alpine zone. New Phytol. 11(2), 37–50 (1912)

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 51935009 and U1608256, in part by National Science and Technology Major Project of the Ministry of Science and Technology of China under Grant 2018ZX04020-001, and in part by the Natural Science Foundation of Zhejiang Province under Grant Y19E050078. The authors would like to thank Dr. Fornaciari and Dr. Fan for providing their executables and insights. The authors also thank Dr. Prasad and Dr. Fornaciari for providing their experimental datasets.

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  1. State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, 310058, Zhejiang, China

    Zhenyu Liu, Xia Liu, Guifang Duan & Jianrong Tan

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  1. Zhenyu Liu
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  2. Xia Liu
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  3. Guifang Duan
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  4. Jianrong Tan
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Correspondence to Guifang Duan.

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Liu, Z., Liu, X., Duan, G. et al. A real-time and precise ellipse detector via edge screening and aggregation. Machine Vision and Applications 31, 64 (2020). https://doi.org/10.1007/s00138-020-01113-1

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