Skip to main content


Evaluating Edge Detection through Boundary Detection


Edge detection has been widely used in computer vision and image processing. However, the performance evaluation of the edge-detection results is still a challenging problem. A major dilemma in edge-detection evaluation is the difficulty to balance the objectivity and generality: a general-purpose edge-detection evaluation independent of specific applications is usually not well defined, while an evaluation on a specific application has weak generality. Aiming at addressing this dilemma, this paper presents new evaluation methodology and a framework in which edge detection is evaluated through boundary detection, that is, the likelihood of retrieving the full object boundaries from this edge-detection output. Such a likelihood, we believe, reflects the performance of edge detection in many applications since boundary detection is the direct and natural goal of edge detection. In this framework, we use the newly developed ratio-contour algorithm to group the detected edges into closed boundaries. We also collect a large data set () of real images with unambiguous ground-truth boundaries for evaluation. Five edge detectors (Sobel, LoG, Canny, Rothwell, and Edison) are evaluated in this paper and we find that the current edge-detection performance still has scope for improvement by choosing appropriate detectors and detector parameters.


  1. 1.

    Julesz B: A method of coding TV signals based on edge detection. Bell System Technology 1959, 38(4):1001–1020.

  2. 2.

    Bowyer KW, Kranenburg C, Dougherty S: Edge detector evaluation using empirical ROC curves. Computer Vision and Image Understanding 2001, 84(1):77–103. 10.1006/cviu.2001.0931

  3. 3.

    Canny J: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1986, 8(6):679–698.

  4. 4.

    Iverson LA, Zucker SW: Logical/linear operators for image curves. IEEE Transactions on Pattern Analysis and Machine Intelligence 1995, 17(10):982–996. 10.1109/34.464562

  5. 5.

    Konishi S, Yuille AL, Coughlan JM, Zhu SC: Statistical edge detection: learning and evaluating edge cues. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003, 25(1):57–74. 10.1109/TPAMI.2003.1159946

  6. 6.

    Prewitt JMS: Object enhancement and extraction. In Picture Processing and Psychopictorics. Edited by: Lipkin BS, Rosenfeld A. Academic Press, New York, NY, USA; 1970:75–149.

  7. 7.

    Rao KR, Ben-Arie J: Optimal edge detection using expansion matching and restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence 1994, 16(12):1169–1182. 10.1109/34.387490

  8. 8.

    Roberts LG: Machine perception of three-dimensional solids. In Optical and Electro-Optical Information Processing. Edited by: Tippett JT. MIT Press, Cambridge, Mass, USA; 1965:159–197.

  9. 9.

    Sobel IE: Camera models and machine perception, Ph.D. dissertation. Stanford University, Stanford, Calif, USA; 1970.

  10. 10.

    Kitchen L, Rosenfeld A: Edge evaluation using local edge coherence. IEEE Transactions on Systems, Man, and Cybernetics 1981, 11(9):597–605.

  11. 11.

    Heath MD, Sarkar S, Sanocki T, Bowyer KW: A robust visual method for assessing the relative performance of edge-detection algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19(12):1338–1359. 10.1109/34.643893

  12. 12.

    Baker S, Nayar SK: Global measures of coherence for edge detector evaluation. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '99), June 1999, Fort Collins, Colo, USA 2: 373–379.

  13. 13.

    Palmer PL, Dabis H, Kitler J: A performance measure for boundary detection algorithms. Computer Vision and Image Understanding 1996, 63(3):476–494. 10.1006/cviu.1996.0036

  14. 14.

    Yitzhaky Y, Peli E: A method for objective edge detection evaluation and detector parameter selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003, 25(8):1027–1033. 10.1109/TPAMI.2003.1217608

  15. 15.

    Zhu Q: Efficient evaluations of edge connectivity and width uniformity. Image and Vision Computing 1996, 14(1):21–34. 10.1016/0262-8856(95)01036-X

  16. 16.

    Jiang XY, Hoover A, Jean-Baptiste G, Goldgof D, Bowyer KW, Bunke H: A methodology for evaluating edge detection techniques for range images. Proceedings of 2nd Asian Conference on Computer Vision (ACCV '95), December 1995, Singapore 2: 415–419.

  17. 17.

    Wang S, Kubota T, Siskind JM, Wang J: Salient closed boundary extraction with ratio contour. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005, 27(4):546–561.

  18. 18.

    Marr D, Hildreth EC: A theory of edge detection. Proceedings of the Royal Society of London. Series B 1980, 207(1167):187–217. 10.1098/rspb.1980.0020

  19. 19.

    Rothwell CA, Mundy JL, Hoffman W, Nguyen V-D: Driving vision by topology. Proceedings of IEEE International Symposium on Computer Vision (ISCV '95), November 1995, Coral Gables, Fla, USA 395–400.

  20. 20.

    Meer P, Georgescu B: Edge detection with embedded confidence. IEEE Transactions on Pattern Analysis and Machine Intelligence 2001, 23(12):1351–1365. 10.1109/34.977560

  21. 21.

    Martin D, Fowlkes C, Tal D, Malik J: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Proceedings of 8th IEEE International Conference on Computer Vision (ICCV '01), July 2001, Vancouver, BC, Canada 2: 416–423.

  22. 22.

    Foley JD, van Dam A, Feiner SK, Hughes JF: Computer Graphics: Principles and Practice in C. 2nd edition. Addison Wesley, Reading, Mass, USA; 1995.

  23. 23.

    Bartels RH, Beatty JC, Barsky BA: An Introduction to Splines for Use in Computer Graphics and Geometric Modelling. Morgan Kaufmann, Los Altos, Calif, USA; 1987.

  24. 24.

    Forsyth DA, Ponce J: Computer Vision: A Modern Approach. Prentice-Hall, Upper Saddle River, NJ, USA; 2003.

Download references

Author information

Correspondence to Song Wang.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Wang, S., Ge, F. & Liu, T. Evaluating Edge Detection through Boundary Detection. EURASIP J. Adv. Signal Process. 2006, 076278 (2006).

Download citation


  • Computer Vision
  • Large Data
  • Quantum Information
  • Edge Detection
  • Challenging Problem