Ellipse Detection through Decomposition of Circular Arcs and Line Segments

  • Thanh Phuong Nguyen
  • Bertrand Kerautret
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6978)

Abstract

In this work we propose an efficient and original method for ellipse detection which relies on a recent contour representation based on arcs and line segments [1]. The first step of such a detection is to locate ellipse candidate with a grouping process exploiting geometric properties of adjacent arcs and lines. Then, for each ellipse candidate we extract a compact and significant representation defined from the segment and arc extremities together with the arc middle points. This representation allows then a fast ellipse detection by using a simple least square technique. Finally some first comparisons with other robust approaches are proposed.

Keywords

Line Segment Tangent Space Error Distance Edge Image Dimensional Parametric Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Nguyen, T.P., Debled-Rennesson, I.: Decomposition of a curve into arcs and line segments based on dominant point detection. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 794–805. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Huang, C.L.: Elliptical feature extraction via an improved hough transform. PRL 10, 93–100 (1989)CrossRefMATHGoogle Scholar
  3. 3.
    Aguado, A., Montiel, M., Nixon, M.: On using directional information for parameter space decomposition in ellipse detection. PR 29, 369–381 (1996)Google Scholar
  4. 4.
    Daul, C., Graebling, P., Hirsch, E.: From the Hough Transform to a New Approach for the Detection and Approximation of Elliptical Arcs. CVIU 72, 215–236 (1998)Google Scholar
  5. 5.
    Chia, A., Leung, M., Eng, H., Rahardja, S.: Ellipse detection with hough transform in one dimensional parametric space. In: ICIP, pp. 333–336 (2007)Google Scholar
  6. 6.
    Lu, W., Tan, J.: Detection of incomplete ellipse in images with strong noise by iterative randomized Hough transform (IRHT). PR 41, 1268–1279 (2008)MATHGoogle Scholar
  7. 7.
    Ahn, S., Rauh, W.: Geometric least squares fitting of circle and ellipse. PRAI 13, 987 (1999)Google Scholar
  8. 8.
    Ciobanu, A., Shahbazkia, H., du Buf, H.: Contour profiling by dynamic ellipse fitting. In: ICPR, vol. 3, pp. 750–753 (2000)Google Scholar
  9. 9.
    Fitzgibbon, A.W., Pilu, M., Fisher, R.B.: Direct least square fitting of ellipses. PAMI 21, 476–480 (1999)CrossRefGoogle Scholar
  10. 10.
    Ahn, S., Rauh, W., Warnecke, H.: Least-squares orthogonal distances fitting of circle, sphere, ellipse, hyperbola, and parabola. PR 34, 2283–2303 (2001)MATHGoogle Scholar
  11. 11.
    Voss, K., Suesse, H.: Invariant fitting of planar objects by primitives. PAMI 19, 80–84 (1997)CrossRefGoogle Scholar
  12. 12.
    Lee, R., Lu, P., Tsai, W.: Moment preserving detection of elliptical shapes in gray-scale images. PRL 11, 405–414 (1990)CrossRefMATHGoogle Scholar
  13. 13.
    Heikkila, J.: Moment and curvature preserving technique for accurate ellipse boundary detection. In: ICPR, vol. 1, pp. 734–737 (1998)Google Scholar
  14. 14.
    Zunic, J., Sladoje, N.: Efficiency of characterizing ellipses and ellipsoids by discrete moments. PAMI 22, 407–414 (2000)CrossRefGoogle Scholar
  15. 15.
    Rosin, P.L.: Measuring shape: ellipticity, rectangularity, and triangularity. Mach. Vis. Appl. 14, 172–184 (2003)CrossRefGoogle Scholar
  16. 16.
    Nguyen, T.P., Debled-Rennesson, I.: A linear method for segmentation of digital arcs. In: Computer Analysis of Images and Patterns. LNCS. Springer, Heidelberg (2011)Google Scholar
  17. 17.
    Latecki, L., Lakamper, R.: Shape similarity measure based on correspondence of visual parts. PAMI 22, 1185–1190 (2000)CrossRefGoogle Scholar
  18. 18.
    Nguyen, T.P., Debled-Rennesson, I.: A discrete geometry approach for dominant point detection. Pattern Recognition 44, 32–44 (2011)CrossRefMATHGoogle Scholar
  19. 19.
    Debled-Rennesson, I., Feschet, F., Rouyer-Degli, J.: Optimal blurred segments decomposition of noisy shapes in linear time. Comp. & Graphics 30, 30–36 (2006)CrossRefMATHGoogle Scholar
  20. 20.
    Gal, O.: Matlab code for ellipse fitting (2003), http://www.mathworks.com/matlabcentral/fileexchange/3215-fitellipse
  21. 21.
    Wu, J.: Robust real-time ellipse detection by direct least-square-fitting. In: CSSE, vol. (1), pp. 923–927 (2008)Google Scholar
  22. 22.
    Libuda, L., Grothues, I., Kraiss, K.: Ellipse detection in digital image data using geometric features, pp. 229–239 (2006)Google Scholar
  23. 23.
    Kerautret, B., Lachaud, J.-O.: Multi-scale analysis of discrete contours for unsupervised noise detection. In: Wiederhold, P., Barneva, R.P. (eds.) IWCIA 2009. LNCS, vol. 5852, pp. 187–200. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Thanh Phuong Nguyen
    • 1
  • Bertrand Kerautret
    • 1
    • 2
  1. 1.ADAGIo teamLORIA, Nancy UniversityVandoeuvreFrance
  2. 2.LAMA (UMR CNRS 5127)University of SavoieFrance

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