Multimedia Tools and Applications

, Volume 75, Issue 2, pp 1099–1133 | Cite as

A novel edge detection approach using a fusion model

  • Xibin Jia
  • Haiyong Huang
  • Yanfeng Sun
  • Jianming Yuan
  • David M. W. Powers


Edge detection is a long standing but still challenging problem. Although there are many effective edge detectors, none of them can obtain ideal edges in every situation. To make the results robust for any image, we propose a new edge detection algorithm based on a two-level fusion model that combines several typical edge detectors together with new proposed edge estimation strategies. At the first level, we select three typical but diverse edge detectors. The edge score is calculated for every pixel in the image based on a consensus measurement by counting positive voting number of approaches. Then results are combined at the second level using the Hadamard product with two additional edge estimations proposed in the paper, based on edge spatial characteristics, where one is binary matrix of the most probable edge distribution and the other is a score matrix based on calculating differences between maxima and minima neighboring intensity change at each point. Comprehensive experiments are conducted on two image databases, and three evaluation methods are employed to measure the performance, viz. F1-measure, ROC and PFOM. Experiments results show that our proposed method outperforms the three standard baseline edge detectors and shows better results than a state-of-the-art method.


Edge detection Fusion Most probable distribution Voting count score matrix Difference score matrix 



We appreciate the support of the Chinese Natural Science Foundation under Grant No. 61070117, No. 61171169 and the Beijing Natural Science Foundation under Grant No. 4122004, No.4132013 and the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions.


  1. 1.
    Abdou IE, Pratt WK (1979) Quantitative design and evaluation of enhancement/thresholding edge detectors. Proc IEEE 67(5):753–763CrossRefGoogle Scholar
  2. 2.
    Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916CrossRefGoogle Scholar
  3. 3.
    Bao P, Zhang L, Wu X (2005) Canny edge detection enhancement by scale multiplication. IEEE Trans Pattern Anal Mach Intell 27(9):1485–1490CrossRefGoogle Scholar
  4. 4.
    Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679–698CrossRefGoogle Scholar
  5. 5.
    Demigny D (2002) On optimal linear filtering for edge detection. IEEE Trans Image Process 11(7):728–737CrossRefGoogle Scholar
  6. 6.
    Farhadi A et al (2010) Every picture tells a story: generating sentences from images, Computer Vision–ECCV 2010. Springer Berlin Heidelberg 15–29Google Scholar
  7. 7.
    Fernández-García NL, Carmona-Poyato A, Medina-Carnicer R, Madrid-Cuevas FJ (2008) Automatic generation of consensus ground truth for the comparison of edge detection techniques. Image Vis Comput 26(4):496–511CrossRefGoogle Scholar
  8. 8.
    Gao W, Yang L, Zhang X, Liu H An improved Sobel edge detection, 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), p 67–71Google Scholar
  9. 9.
    Julesz B (1959) A method of coding TV signals based on edge detection. Bell Syst Technol 38(4):1001–1020CrossRefGoogle Scholar
  10. 10.
    Laligant O, Truchetet F (2010) A nonlinear derivative scheme applied to edge detection. IEEE Trans Pattern Anal Mach Intell 32(2):242–257CrossRefGoogle Scholar
  11. 11.
    Laligant O, Truchetet F, Meriaudeau F (2007) Regularization preserving localization of close edges. IEEE Signal Process Lett 14(3):185–188CrossRefGoogle Scholar
  12. 12.
    Lewis TW, Powers DMW (2004) Sensor fusion weighting measures in audio-visual speech recognition. Proc. 27th Australasian Conference on Computer Science 26:305–314Google Scholar
  13. 13.
    Luo RC, Yih C-C, Su K (2002) Multisensor fusion and integration: approaches, applications, and future research directions. IEEE Sensors J 2(2):107–119CrossRefGoogle Scholar
  14. 14.
    Marr D, Hildreth E (1980) Theory of edge detection. Proc R Soc Lond Ser B Biol Sci 207(1167):187–217CrossRefGoogle Scholar
  15. 15.
    Mathworks (2002) Image processing toolbox for use with MATLAB. User’s guide version 3Google Scholar
  16. 16.
    Melgani F (2006) Robust image binarization with ensembles of thresholding algorithms. J Electron Imaging 15(2), 023010-1-023010-11CrossRefGoogle Scholar
  17. 17.
    Novak CL, Shafer SA (1987) Color edge detection. In: Proc. DARPA Image Understanding Workshop I:35–37Google Scholar
  18. 18.
    Ou Y, GuangZhi D (2011) Color edge detection based on data fusion technology in presence of Gaussian noise. Procedia Eng 15:2439–2443CrossRefGoogle Scholar
  19. 19.
    Powers DMW (2012) ROC-ConCert: ROC-based measurement of consistency and certainty. Spring Congress on Engineering and Technology (S-CET)Google Scholar
  20. 20.
    Powers DMW (2013) AdaBook & MultiBook: adaptive boosting with chance correction, 10th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Reykjavic, JulyGoogle Scholar
  21. 21.
    Prewitt JMS (1970) Object enhancement and extraction, picture processing and psychopictorics. Academic PressGoogle Scholar
  22. 22.
    Ren J et al (2010) Fusion of intensity and inter-component chromatic difference for effective and robust colour edge detection. IET Image Process 4(4):294–301CrossRefGoogle Scholar
  23. 23.
    Roberts LG (1963) Machine perception of three-dimensional solids, No. TR315. Massachusetts Inst. of Tech Lexington Lincoln LabGoogle Scholar
  24. 24.
    Smith SM, Brady JM (1997) SUSAN—a new approach to low level image processing. Int J Comput Vis 23(1):45–78CrossRefGoogle Scholar
  25. 25.
    Sobel I (1970) Camera models and machine perception, No. AIM-121. Stanford Univ. Calif. Dept. of Computer ScienceGoogle Scholar
  26. 26.
    Swets JA (1996) Signal detection theory and ROC analysis in psychology and diagnostics: collected papers. Lawrence Erlbaum Associates, IncGoogle Scholar
  27. 27.
    Yitzhaky Y, Peli E (2003) A method for objective edge detection evaluation and detector parameter selection. IEEE Trans Pattern Anal Mach Intell 25(8):1027–1033CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Xibin Jia
    • 1
  • Haiyong Huang
    • 1
  • Yanfeng Sun
    • 1
  • Jianming Yuan
    • 1
  • David M. W. Powers
    • 1
    • 2
  1. 1.Beijing Municipal Key Laboratory of Multimedia and Intelligent Software TechnologyBeijing University of TechnologyBeijingPeople’s Republic of China
  2. 2.School of Computer Science, Engineering and MathematicsFlinders University of South AustraliaAdelaideAustralia

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