Circle Detection Performance Evaluation Revisited

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9657)

Abstract

Circle and circular arc detection in images have been a long standing topic in image analysis. It finds numerous applications for both scanned document images as well as in photographic images. As a result, circle detection algorithms are published regularly and benchmarking data sets and contests have been organized on a regular basis over the last decades. Unfortunately, they have not been able to achieve a very clear image establishing which approaches perform best and under what exact conditions.

This paper contributes to circle and arc detection, by providing an open and fully reproducible framework for benchmarking and evaluating circle and circular arc detection methods. It builds upon the current state of the art and commonly used metrics by providing a complementary approach through the introduction of synthetic evaluation data for benchmarking versus two noise types at gradually varying noise levels and new performance metrics that are compatible with previous evaluation approaches.

Notes

Acknowledgment

The authors would like to thank Télécom Nancy students G. Humbert and Y. Jardin for their preliminary work, and all authors who were solicited and who kindly provided their source code, binary code or other contribution allowing us to fully evaluate their methods. E. Barney Smith was funded under the “Chercheur d’excellence” program by the Région Lorraine.

References

  1. 1.
    Akinlar, C., Topal, C.: EDLines: a real-time line segment detector with a false detection control. Pattern Recognit. Lett. 32(13), 1633–1642 (2011)CrossRefGoogle Scholar
  2. 2.
    Akinlar, C., Topal, C.: EDCircles: a real-time circle detector with a false detection control. Pattern Recognit. 46, 725–740 (2013)CrossRefGoogle Scholar
  3. 3.
    Akinlar, C., Topal, C.: EDCircles Web Interface (2015). http://ceng.anadolu.edu.tr/CV/EDCircles/demo.aspx. Accessed 6 May 2015
  4. 4.
    Al-Khaffaf, H.S.M., Talib, A.Z., Osman, M.A., Wong, P.L.: GREC’09 arc segmentation contest: performance evaluation on old documents. In: Ogier, J.-M., Liu, W., Lladós, J. (eds.) GREC 2009. LNCS, vol. 6020, pp. 251–259. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13728-0_23 CrossRefGoogle Scholar
  5. 5.
    Al-Khaffaf, H.S.M., Talib, A.Z., Osman, M.A.: Final report of GREC’11 arc segmentation contest: performance evaluation on multi-resolution scanned documents. In: Kwon, Y.-B., Ogier, J.-M. (eds.) GREC 2011. LNCS, vol. 7423, pp. 187–197. Springer, Heidelberg (2013). doi:10.1007/978-3-642-36824-0_18 CrossRefGoogle Scholar
  6. 6.
    Baird, H.S.: Document image defect models. In: Proceedings of the IAPR Workshop on Syntactic and Structural Pattern Recognition, pp. 13–15. Murray Hill, NJ, June 1990. Reprinted: Baird, H.S., Bunke, H., Yamamoto, K. (eds.) Structured Document Image Analysis. Springer, New York (1992)Google Scholar
  7. 7.
    Bukhari, S.S., Al-Khaffaf, H.S.M., Shafait, F., Osman, M.A., Talib, A.Z., Breuel, T.M.: Final report of GREC’13 arc and line segmentation contest. In: Lamiroy, B., Ogier, J.-M. (eds.) GREC 2013. LNCS, vol. 8746, pp. 234–239. Springer, Heidelberg (2014). doi:10.1007/978-3-662-44854-0_18 Google Scholar
  8. 8.
    Desolneux, A., Moisan, L., Morel, J.: From Gestalt Theory to Image Analysis: A Probabilisitc Approach. Springer, New York (2008)CrossRefGoogle Scholar
  9. 9.
    Hough, P.: Methods and means for recognizing complex patterns (1962)Google Scholar
  10. 10.
    Jia, L.Q., Peng, C.Z.: A new circle detection method based on parallel operator. In: 2012 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 3, pp. 1085–1090 (2012)Google Scholar
  11. 11.
    Jia, L.Q., Peng, C.Z., Liu, H.M., Wang, Z.H.: A fast randomized circle detection algorithm. In: 2011 4th International Congress on Image and Signal Processing (CISP), vol. 2, pp. 820–823 (2011)Google Scholar
  12. 12.
    Lamiroy, B., Lopresti, D.: An open architecture for end-to-end document analysis benchmarking. In: 2011 International Conference on Document Analysis and Recognition (ICDAR), pp. 42–47, September 2011Google Scholar
  13. 13.
    Lamiroy, B.: Interpretation, evaluation and the semantic gap.. What if we were on a side-track? In: Lamiroy, B., Ogier, J.-M. (eds.) GREC 2013. LNCS, vol. 8746, pp. 221–233. Springer, Heidelberg (2014). doi:10.1007/978-3-662-44854-0_17 Google Scholar
  14. 14.
    Lamiroy, B., Guebbas, Y.: Robust and precise circular arc detection. In: Ogier, J.-M., Liu, W., Lladós, J. (eds.) GREC 2009. LNCS, vol. 6020, pp. 49–60. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13728-0_5 CrossRefGoogle Scholar
  15. 15.
    Liu, W.: Report of the arc segmentation contest. In: Lladós, J., Kwon, Y.-B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 364–367. Springer, Heidelberg (2004). doi:10.1007/978-3-540-25977-0_33 CrossRefGoogle Scholar
  16. 16.
    Wenyin, L.: The third report of the arc segmentation contest. In: Liu, W., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 358–361. Springer, Heidelberg (2006). doi:10.1007/11767978_32 CrossRefGoogle Scholar
  17. 17.
    Liu, W., Dori, D.: A protocol for performance evaluation of line detection algorithms. Mach. Vis. Appl. 9, 240–250 (1997)CrossRefGoogle Scholar
  18. 18.
    Liu, W., Zhai, J., Dori, D.: Extended summary of the arc segmentation contest. In: Blostein, D., Kwon, Y.-B. (eds.) GREC 2001. LNCS, vol. 2390, pp. 343–349. Springer, Heidelberg (2002). doi:10.1007/3-540-45868-9_30 CrossRefGoogle Scholar
  19. 19.
    Liu, W., Zhai, J., Dori, D., Long, T.: System for performance evaluation of arc segmentation algorithms. In: Proceedings of the CVPR Workshop Empirical Evaluation in Computer Vision (2001)Google Scholar
  20. 20.
    McGillivary, C., Hale, C., Smith, E.H.B.: Edge noise in document images. In: Proceedings of the 3rd Workshop on Analytics for Noisy Unstructured Text Data, pp. 17–24 (2009)Google Scholar
  21. 21.
    Nixon, M., Aguado, A.: Feature Extraction & Image Processing, 2nd edn. Academic Press, Oxford (2008)Google Scholar
  22. 22.
    OpenCV: Hough Circle Transform (2015). http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.html. Accessed 13 May 2015
  23. 23.
    Shafait, F., Keysers, D., Breuel, T.M.: GREC 2007 arc segmentation contest: evaluation of four participating algorithms. In: Liu, W., Lladós, J., Ogier, J.-M. (eds.) GREC 2007. LNCS, vol. 5046, pp. 310–320. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88188-9_29 CrossRefGoogle Scholar
  24. 24.
    Wolfram MathWorld: Circle-Circle Intersection (2015). http://mathworld.wolfram.com/Circle-CircleIntersection.html. Accessed 13 May 2015

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Electrical and Computer Engineering DepartmentBoise State UniversityBoiseUSA
  2. 2.Université de Lorraine – Loria (UMR 7503)Vandœuvre-lès-Nancy CedexFrance

Personalised recommendations