OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Editor: Franco Gori
  • Vol. 28, Iss. 4 — Apr. 1, 2011
  • pp: 581–589

Robust ellipse detection based on hierarchical image pyramid and Hough transform

Chung-Fang Chien, Yu-Che Cheng, and Ta-Te Lin  »View Author Affiliations

JOSA A, Vol. 28, Issue 4, pp. 581-589 (2011)

View Full Text Article

Enhanced HTML    Acrobat PDF (1028 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



In this research we propose a fast and robust ellipse detection algorithm based on a multipass Hough transform and an image pyramid data structure. The algorithm starts with an exhaustive search on a low-resolution image in the image pyramid using elliptical Hough transform. Then the image resolution is iteratively increased while the candidate ellipses with higher resolution are updated at each step until the original image resolution is reached. After removing the detected ellipses, the Hough transform is repeatedly applied in multiple passes to search for remaining ellipses, and terminates when no more ellipses are found. This approach significantly reduces the false positive error of ellipse detection as compared with the conventional randomized Hough transform method. The analysis shows that the computing complexity of this algorithm is Θ ( n 5 / 2 ) , and thus the computation time and memory requirement are significantly reduced. The developed algorithm was tested with images containing various numbers of ellipses. The effects of noise-to-signal ratio combined with various ellipse sizes on the detection accuracy were analyzed and discussed. Experimental results revealed that the algorithm is robust to noise. The average detection accuracies were all above 90% for images with less than seven ellipses, and slightly decreased to about 80% for images with more ellipses. The average false positive error was less than 2%.

© 2011 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(100.5010) Image processing : Pattern recognition
(100.3008) Image processing : Image recognition, algorithms and filters
(100.4994) Image processing : Pattern recognition, image transforms

ToC Category:
Image Processing

Original Manuscript: September 28, 2010
Revised Manuscript: December 28, 2010
Manuscript Accepted: January 28, 2011
Published: March 16, 2011

Chung-Fang Chien, Yu-Che Cheng, and Ta-Te Lin, "Robust ellipse detection based on hierarchical image pyramid and Hough transform," J. Opt. Soc. Am. A 28, 581-589 (2011)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. P. V. C. Hough, “Method and means for recognizing complex patterns,” U.S. patent 3,069,654 (18 December 1962).
  2. R. O. Duda and P. E. Hart, “Use of the Hough transformation to detect lines and curves in pictures,” Commun. ACM 15, 11–15 (1972). [CrossRef]
  3. D. H. Ballard, “Generalizing the Hough transform to detect arbitrary shapes,” Pattern Recogn. 13, 111–122 (1981). [CrossRef]
  4. S. Tsuji and F. Matsumoto, “Detection of ellipses by a modified Hough transformation,” IEEE Trans. Comput. C-27, 777–781(1978). [CrossRef]
  5. C. Kimme, D. Ballard, and J. Sklansky, “Finding circles by an array of accumulators,” Commun. ACM 18, 120–122 (1975). [CrossRef]
  6. H. Li, M. A. Lavin, and R. J. Le Master, “Fast Hough transform: a hierarchical approach,” Comput. Vision Graphics Image Process. 36, 139–161 (1986). [CrossRef]
  7. H. K. Muammar and M. Nixon, “Tristage Hough transform for multiple ellipse extraction,” IEE Proc. E 138, 27–35 (1991). [CrossRef]
  8. N. Guil and L. Zapata, “Lower order circle and ellipse Hough transform,” Pattern Recogn. 30, 1729–1744 (1997). [CrossRef]
  9. J. Illingworth and J. Kittler, “A survey of the Hough transform,” Comput. Vision Graphics Image Process. 44, 87–116 (1988). [CrossRef]
  10. R. A. McLaughlin, “Randomized Hough transform: improved ellipse detection with comparison,” Pattern Recogn. Lett. 19, 299–305 (1998). [CrossRef]
  11. Y. Lei and K. C. Wong, “Ellipse detection based on symmetry,” Pattern Recogn. Lett. 20, 41–47 (1999). [CrossRef]
  12. A. A. Sewisy and F. Leberl, “Detection ellipses by finding lines of symmetry in the images via a Hough transform applied to straight lines,” Image Vision Comput. 19, 857–866 (2001). [CrossRef]
  13. S. C. Zhang and Z. Q. Liu, “A robust, real-time ellipse detector,” Pattern Recogn. 38, 273–287 (2005). [CrossRef]
  14. J. Yao, N. Kharma, and P. Grogono, “A multi-population genetic algorithm for robust and fast ellipse detection,” Pattern Anal. Appl. 8, 149–162 (2005). [CrossRef]
  15. G. Song and H. Wang, “A fast and robust ellipse detection algorithm based on pseudo-random sample consensus,” in Computer Analysis of Images and Patterns, Vol.  4673 of Lecture Notes in Computer Science (Springer, 2007), pp. 669–676.
  16. W. Kaewapichai and P. Kaewtrakulpong, “Robust ellipse detection by fitting randomly selected edge patches,” World Acad. Sci. Eng. Technol. 48, 30–33 (2008).
  17. F. Mai, Y. S. Hung, H. Zhong, and W. F. Sze, “A hierarchical approach for fast and robust ellipse extraction,” Pattern Recogn. 41, 2512–2524 (2008). [CrossRef]
  18. M. A. Rashwan, M. S. Elsherif, and A. M. Elsayad, “Pyramid data structures for on-line image progressive transmission,” in 36th Mid-West Symposium on Circuits and Systems (IEEE, 1993), pp. 103–106.
  19. L.Uhr, ed., Parallel Computer Vision, (Academic, 1987).
  20. M. Wu, J. Sun, J. Zhou, and G. Xue, “Color constancy based on texture pyramid matching and regularized local regression,” J. Opt. Soc. Am. A 27, 2097–2015 (2010). [CrossRef]
  21. M. Goldberg and L. Wang, “Comparative performance of pyramid data structures for progressive image transmission,” IEEE Trans. Commun. 39, 540–548 (1991). [CrossRef]
  22. W. D. Hofmann and D. E. Troxel, “Making progressive transmission adaptive,” IEEE Trans. Commun. 34, 806–813 (1986). [CrossRef]
  23. P. Meer, “Stochastic image pyramids,” Comput. Vision Graphics Image Process. 45, 269–294 (1989). [CrossRef]
  24. K. R. Sloan and S. L. Tanimoto, “Progressive refinement of raster images,” IEEE Trans. Comput. C-28, 871–875 (1979). [CrossRef]
  25. L. Wang and M. Goldberg, “Progressive image transmission using vector quantization on images in pyramid form,” IEEE Trans. Commun. 37, 1339–1349 (1989). [CrossRef]
  26. G. Bongiovanni, C. Guerra, and S. Levialdi, “Computing the Hough transform on a pyramid architecture,” Machine Vision Appl. 3, 117–123 (1990). [CrossRef]
  27. C. Espinosa and M. A. Perkowski, “Hierarchical Hough transform based on pyramidal architecture,” in Eleventh Annual International Phoenix Conference on Computer and Communications 1992 (IEEE, 1992), pp. 0743–0750.
  28. J. M. Jolion and A. Rosenfeld, “An O(log⁡n) pyramid Hough transform,” Pattern Recogn. Lett. 9, 343–349 (1989). [CrossRef]
  29. D. Schreiber and Y. Luo, “Seat detection in a car for a smart airbag application,” Pattern Recogn. Lett. 28, 534–544(2007). [CrossRef]
  30. S. R. Deans, “Hough transform from the Radon transform,” IEEE Trans. Pattern Anal. Machine Intell. PAMI-3, 185–188 (1981). [CrossRef]
  31. V. F. Leavers, “Which Hough transform?” Comput. Vision Graphics Image Process. Image Underst. 58, 250–264 (1993). [CrossRef]
  32. G. B. Thomas and R. L. Finney, Calculus and Analytic Geometry (Addison-Wesley, 1996).
  33. C. F. Chien and T. T. Lin, “Leaf area measurement of selected vegetable seedlings using elliptical Hough transform,” Trans. Am. Soc. Agric. Eng. 45, 1669–1677 (2002).
  34. C. F. Chien, “Non-destructive measurement of selected vegetable seedlings using 3D machine vision,” Ph.D. dissertation (Bio-Industrial Mechatronics Engineering Department, National Taiwan University, 2003).
  35. R. M. Haralick and L. G. Shapiro, Computer and Robot Vision (Addison-Wesley, 1992), Vol.  I.
  36. C. T. Ho and L. H. Chen, “A fast ellipse/circle detector using geometric symmetry,” Pattern Recogn. 28, 117–124 (1995). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited