Eurofuse 2011 pp 401-412 | Cite as

Objective Comparison of Some Edge Detectors Based on Fuzzy Morphologies

  • M. González-Hidalgo
  • S. Massanet
  • A. Mir
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 107)

Abstract

In this paper a comparative analysis of several edge detectors based on diverse fuzzy morphologies is performed. In addition, two different processes in order to transform a fuzzy edge image to a thin binary edge image are studied, a recently introduced unsupervised hysteresis based on the determination of a “instability zone” on the histogram and a fuzzy Atanassov’s based threshold. The comparison is made according to some performance measures, such as Pratt’s figure of merit and the ρ-coefficient. The goodness of the employed binarization methods is studied depending on their capability to obtain the best threshold values according to these measures.

Keywords

Edge Detector Edge Image Edge Pixel Objective Comparison Morphological Operator 
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.

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References

  1. 1.
    Bloch, I., Maître, H.: Fuzzy mathematical morphologies: a comparative study. Pattern Recognition 28, 1341–1387 (1995)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bodenhofer, U.: A unified framework of opening and closure operators with respect to arbitrary fuzzy relations. Soft Computing 7, 220–227 (2003)MATHCrossRefGoogle Scholar
  3. 3.
    Bowyer, K., Kranenburg, C., Dougherty, S.: Edge detector evaluation using empirical ROC curves. Computer Vision and Pattern Recognition 1, 354–359 (1999)Google Scholar
  4. 4.
    Bustince, H., Barrenechea, E., Pagola, M., Fernandez, J.: Interval-valued fuzzy sets constructed from matrices: Application to edge detection. Fuzzy Sets and Systems 160(13), 1819–1840 (2009)MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)CrossRefGoogle Scholar
  6. 6.
    De Baets, B.: Fuzzy morphology: A logical approach. In: Ayyub, B.M., Gupta, M.M. (eds.) Uncertainty Analysis in Engineering and Science: Fuzzy Logic, Statistics, and Neural Network Approach, pp. 53–68. Kluwer Academic Publishers, Norwell (1997)Google Scholar
  7. 7.
    De Baets, B., Kerre, E., Gupta, M.: The fundamentals of fuzzy mathematical morfologies part I: basics concepts. International Journal of General Systems 23, 155–171 (1995)MATHCrossRefGoogle Scholar
  8. 8.
    González-Hidalgo, M., Massanet, S.: Closing and opening based on discrete t-norms. Applications to Natural Image Analysis. Accepted in EUSFLAT-LFA 2011 (2011)Google Scholar
  9. 9.
    González-Hidalgo, M., Massanet, S.: Towards an objective edge detection algorithm based on discrete t-norms. Accepted in EUSFLAT-LFA 2011 (2011)Google Scholar
  10. 10.
    González-Hidalgo, M., Massanet, S., Torrens, J.: Discrete t-norms in a fuzzy mathematical morphology: Algebraic properties and experimental results. In: Proceedings of WCCI-FUZZ-IEEE, Barcelona, Spain, pp. 1194–1201 (2010)Google Scholar
  11. 11.
    González-Hidalgo, M., Mir-Torres, A., Ruiz-Aguilera, D., Torrens, J.: Edge-images using a uninorm-based fuzzy mathematical morphology: Opening and closing. In: Tavares, J., Jorge, N. (eds.) Advances in Computational Vision and Medical Image Processing, Computational Methods in Applied Sciences. ch. 8, vol. 13, pp. 137–157. Springer, Netherlands (2009)CrossRefGoogle Scholar
  12. 12.
    González-Hidalgo, M., Mir-Torres, A., Ruiz-Aguilera, D., Torrens, J.: Image analysis applications of morphological operators based on uninorms. In: Proceedings of the IFSA-EUSFLAT 2009 Conference, Lisbon, Portugal, pp. 630–635 (2009)Google Scholar
  13. 13.
    Grigorescu, C., Petkov, N., Westenberg, M.A.: Contour detection based on nonclassical receptive field inhibition. IEEE Transactions on Image Processing 12(7), 729–739 (2003)CrossRefGoogle Scholar
  14. 14.
    Kovesi, P.D.: MATLAB and Octave functions for computer vision and image processing. Centre for Exploration Targeting, School of Earth and Environment, The University of Western Australia, http://www.csse.uwa.edu.au/~pk/research/matlabfns/
  15. 15.
    Medina-Carnicer, R., Muñoz-Salinas, R., Yeguas-Bolivar, E., Diaz-Mas, L.: A novel method to look for the hysteresis thresholds for the Canny edge detector. Pattern Recognition 44(6), 1201–1211 (2011)CrossRefGoogle Scholar
  16. 16.
    Nachtegael, M., Kerre, E.: Classical and fuzzy approaches towards mathematical morphology. In: Kerre, E.E., Nachtegael, M. (eds.) Fuzzy techniques in image processing. ch. 1, vol. (52), pp. 3–57. Physica-Verlag, New York (2000)Google Scholar
  17. 17.
    Papari, G., Petkov, N.: Edge and line oriented contour detection: State of the art. Image and Vision Computing 29(2-3), 79–103 (2011)CrossRefGoogle Scholar
  18. 18.
    Pratt, W.K.: Digital Image Processing, 4th edn. Wiley Interscience, Hoboken (2007)CrossRefGoogle Scholar
  19. 19.
    Serra, J.: Image analysis and mathematical morphology, vol. 1, 2. Academic Press, London (1982/1988)Google Scholar
  20. 20.
    Sola, H.B., Tartas, E.B., Pagola, M., Orduna, R.: Image thresholding computation using Atanassov’s intuitionistic fuzzy sets. JACIII 11(2), 187–194 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • M. González-Hidalgo
    • 1
  • S. Massanet
    • 1
  • A. Mir
    • 1
  1. 1.Dept. of Math. and Comp. ScienceUniversity of the Balearic IslandsPalma de MallorcaSpain

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