research-article

A Method for Objective Edge Detection Evaluation and Detector Parameter Selection

Authors:

Yitzhak Yitzhaky and

Eli PeliAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 25, Issue 8

Pages 1027 - 1033

https://doi.org/10.1109/TPAMI.2003.1217608

Published: 01 August 2003 Publication History

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.

References

[1]

M. 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.

Digital Library

[2]

M. Heath S. Sarkar T. Sanocki and K.W. Bowyer, “Comparison of Edge Detectors: A Methodology and Initial Study,” Computer Vision and Image Understanding, vol. 69, no. 1, pp. 38-54 Jan. 1998.

Digital Library

[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.

Digital Library

[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.

Digital Library

[6]

T. Kanungo M.Y. Jaisimha J. Palmer and R.M. Haralick, “A Methodology for Quantitative Performance Evaluation of Detection Algorithms,” IEEE Trans. Image Processing, vol. 4, no. 12, pp. 1667-1673, Dec. 1995.

Digital Library

[7]

K. Bowyer C. Kranenburg and S. Dougherty, “Edge Detector Evaluation Using Empirical ROC Curves,” Computer Vision and Image Understanding, vol. 84, no. 1, pp. 77-103, Oct. 2001.

Digital Library

[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.

Digital Library

[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.

Digital Library

[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. Dordrecht, The Netherlands: Kluwer Academic, 1994.

Digital Library

[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.

Digital Library

[17]

J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Analysis and Machine intelligence, vol. 8, no. 6, pp. 679-698, Nov. 1986.

Digital Library

[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/~kranenbu/roc.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.

Digital Library

[23]

The Computer Vision/Image Analysis Research Laboratory, Univ. of South Florida, http://marathon.csee.usf.edu/edge/edge_detection.html, Dec. 2002.

[24]

The Vision Rehabilitation Laboratory, Schepens Eye Research Inst., Boston Mass., http://www.eri.harvard.edu/faculty/peli/papers/Appendix_ EdgeDetectionEval.pdf, Dec. 2002.

Cited By

Zhou CYuan CWang HLi LOehmcke SLiu JPeng J(2023)Multi-scale pseudo labeling for unsupervised deep edge detectionKnowledge-Based Systems10.1016/j.knosys.2023.111057280:COnline publication date: 25-Nov-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.111057
Fuentes-Alventosa AGómez-Luna JMedina-Carnicer R(2022)GUD-Canny: a real-time GPU-based unsupervised and distributed Canny edge detectorJournal of Real-Time Image Processing10.1007/s11554-022-01208-019:3(591-605)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11554-022-01208-0
Ramadevi VManjunatha Chari K(2019)FPGA realization of an efficient image scalar with modified area generation techniqueMultimedia Tools and Applications10.1007/s11042-019-7592-678:16(23707-23732)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s11042-019-7592-6
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Index Terms

A Method for Objective Edge Detection Evaluation and Detector Parameter Selection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Interest point and salient region detections
      2. Image and video acquisition
        Epipolar geometry

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Information

Published In

cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 25, Issue 8

August 2003

112 pages

ISSN:0162-8828

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Copyright © Copyright © 2003 IEEE. All Rights Reserved.

Publisher

IEEE Computer Society

United States

Publication History

Published: 01 August 2003

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Cited By

Zhou CYuan CWang HLi LOehmcke SLiu JPeng J(2023)Multi-scale pseudo labeling for unsupervised deep edge detectionKnowledge-Based Systems10.1016/j.knosys.2023.111057280:COnline publication date: 25-Nov-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.111057
Fuentes-Alventosa AGómez-Luna JMedina-Carnicer R(2022)GUD-Canny: a real-time GPU-based unsupervised and distributed Canny edge detectorJournal of Real-Time Image Processing10.1007/s11554-022-01208-019:3(591-605)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11554-022-01208-0
Ramadevi VManjunatha Chari K(2019)FPGA realization of an efficient image scalar with modified area generation techniqueMultimedia Tools and Applications10.1007/s11042-019-7592-678:16(23707-23732)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s11042-019-7592-6
Magnier B(2018)Edge detectionMultimedia Tools and Applications10.1007/s11042-017-5127-677:8(9489-9533)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s11042-017-5127-6
Lin LLin WHuang S(2018)Group object detection and tracking by combining RPCA and fractal analysisSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2329-122:1(231-242)Online publication date: 1-Jan-2018
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Chan KEngelke UAbhayasinghe N(2017)An edge detection framework conjoining with IMU data for assisting indoor navigation of visually impaired personsExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.09.00767:C(272-284)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.eswa.2016.09.007
Lopez-Molina CBustince HDe Baets B(2016)Separability Criteria for the Evaluation of Boundary Detection BenchmarksIEEE Transactions on Image Processing10.1109/TIP.2015.251028425:3(1047-1055)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1109/TIP.2015.2510284
Lopez-Molina CDe Baets BBustince H(2016)Twofold consensus for boundary detection ground truthKnowledge-Based Systems10.1016/j.knosys.2016.01.03398:C(162-171)Online publication date: 15-Apr-2016
https://dl.acm.org/doi/10.1016/j.knosys.2016.01.033
Jia XHuang HSun YYuan JPowers D(2016)A novel edge detection approach using a fusion modelMultimedia Tools and Applications10.1007/s11042-014-2359-675:2(1099-1133)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1007/s11042-014-2359-6
Lang JBecke MSchlegl T(2015)A Systematic Approach for the Parameterisation of the Kernel-Based Hough Transform Using a Human-Generated Ground TruthProceedings, Part I, of the 8th International Conference on Intelligent Robotics and Applications - Volume 924410.1007/978-3-319-22879-2_44(473-486)Online publication date: 24-Aug-2015
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