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A Method for Objective Edge Detection Evaluation and Detector Parameter Selection
August 2003 (vol. 25 no. 8)
pp. 1027-1033

Abstract—Subjective evaluation by human observers is usually used to analyze and select an edge detector parametric setup when real-world images are considered. In this paper, we propose a statistical objective performance analysis and detector parameter selection, using detection results produced by different detector parameters. Using the correspondence between the different detection results, an estimated best edge map, utilized as an estimated ground truth (EGT), is obtained. This is done using both a receiver operating characteristics (ROC) analysis and a Chi-square test, and considers the trade off between information and noisiness in the detection results. The best edge detector parameter set (PS) is then selected by the same statistical approach, using the EGT. Results are demonstrated for several edge detection techniques, and compared to published subjective evaluation results. The method developed here suggests a general tool to assist in practical implementations of parametric edge detectors where an automatic process is required.

[1] M.D. Heath, S. Sarkar, T. Sanocki, and K.W. Bowyer, A Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 12, pp. 1338-1359, Dec. 1997.
[2] M. Heath, S. Sarkar, T. Sanocki, and K. Bowyer, “Comparison of Edge Detectors: A Methodology and Initial Study,” Computer Vision, Graphics, and Image Understanding, vol. 69, no. 1, pp. 38-54, 1998.
[3] M.C. Shin, D. Goldgof, and K.W. Bowyer, Comparison of Edge Detector Performance through Use in an Object Recognition Task Computer Vision and Image Understanding, vol. 84, no. 1, pp. 160-178, Oct. 2001.
[4] T. Peli and D. Malah, A Study of Edge Detection Algorithms Computer Graphics and Image Processing, vol. 20, pp. 1-21, 1982.
[5] J.R. Farm and E.W. Deutsch, On The Quantitative Evaluation of Edge Detection Schemes and Their Comparison with Human Performance IEEE Trans. Computer, vol. 24, no. 6, pp. 616-628, June 1975.
[6] T. Kanungo, M.Y. Jaisimha, and R.M. Haralick, "A Methodology for Quantitative Performance Evaluation of Detection Algorithms," IEEE Trans. Image Processing, vol. 4, pp. 1,667-1,674, Dec. 1995.
[7] K.W. Bowyer, C. Kranenburg, and S. Dougherty, “Edge Detector Evaluation Using Empirical ROC Curves,” Computer Vision and Image Understanding, vol. 84, no. 10, pp 77-103, 2001.
[8] M.C. Shin, D. Goldgof, and K.W. Bowyer, An Objective Comparison Methodology of Edge Detection Algorithms Using a Structure from Motion Task Empirical Evaluation Techniques in Computer Vision, IEEE CS, pp. 235-254, 1998.
[9] L. Kitchen and A. Rosenfeld, Edge Evaluation Using Local Edge Coherence IEEE Trans. Systems, Man, and Cybernetics, vol. 11, no. 9, pp. 597-605, Sept. 1981.
[10] R.M. Haralick and J.S.J. Lee, Context Dependent Edge Detection and Evaluation Pattern Recognition, vol. 23, no. 1/2, pp. 1-19, 1990.
[11] Q. Zhu, Efficient Evaluations of Edge Connectivity and Width Uniformity Image and Vision Computing, vol. 14, pp. 21-34, 1996.
[12] E. Peli, Feature Detection Algorithm Based on a Visual System Model Proc. IEEE, vol. 90, pp. 78-93, 2002.
[13] D. Ziouand, S. Tabbone, Edge Detection Techniques- An Overview Technical Report no. 195, Dept. of Math. and Informatique, Universite de Sherbrooke, 1997.
[14] T. Lindeberg, Scale-Space Theory in Computer Vision. Kluwer Academic, 1994.
[15] V. Berzins, Accuracy of Laplacian Edge Detectors Computer Vision, Graphics, and Image Processing, vol. 27, pp. 195-210, 1984.
[16] M. Shahand, A. Sood, and R. Jain, Pulse and Staircase Edge Models Computer Vision, Graphics, and Image Processing vol. 34, pp. 321-343, 1986.
[17] J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, June 1986.
[18] D. Marr and E.C. Hildreth, Theory of Edge Detection Proc. Royal Soc., London B, vol. 207, pp. 187-217, 1980.
[19] The Computer Vision/Image Analysis Research Laboratory, Univ. of South Florida,http://figment.csee.usf.edu/~kranenburoc.html , Jan. 2002.
[20] H.C. Kraemer, Evaluating Medical Tests: Objective and Quantitative Guidelines. Newbury Park, Calif.: Sage Publications, 1992.
[21] N.A. Macmillan and C.D. Creelman, Detection Theory: A User's Guide. Cambridge: Cambridge Univ. Press, 1991.
[22] M.R. Everingham, H. Muller, and B.T. Thomas, Evaluating Image Segmentation Algorithms Using the Pareto Front Proc. Seventh European Conf. Computer Vision, pp. IV:34-48, May 2002.
[23] The Computer Vision/Image Analysis Research Laboratory, Univ. of South Florida,http://marathon.csee.usf.edu/edgeedge_detection.html , Dec. 2002.
[24] The Vision Rehabilitation Laboratory, Schepens Eye Research Inst., Boston Mass.,http://www.eri.harvard.edu/faculty/peli/ papersAppendix_ EdgeDetectionEval.pdf, Dec. 2002.

Index Terms:
Edge detection evaluation, detector parameters, receiver operating characteristics.
Citation:
Yitzhak Yitzhaky, Eli Peli, "A Method for Objective Edge Detection Evaluation and Detector Parameter Selection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 8, pp. 1027-1033, Aug. 2003, doi:10.1109/TPAMI.2003.1217608
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