Detecting Edge Using Support Vector Machine


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The edge detection is used in many applications in image processing. It is currently crucial technique of image processing. There are various methods for promoting edge detection. Here, it is presented that edge detection can be achieved using Support Vector Machine (SVM). Supervised learning method is applied. Laplacian edge detector is an instructor of Support Vector Machine. In this research, it is presented that any classical method can be applied for training of SVM as edge detector.



Advanced Materials Research (Volumes 588-589)

Edited by:

Lawrence Lim




J. P. Yeh, "Detecting Edge Using Support Vector Machine", Advanced Materials Research, Vols. 588-589, pp. 974-977, 2012

Online since:

November 2012





[1] C. Cortes and V. Vapnik: Support Vector Networks, Machine Learning, 20 , 273-297 (1995).

[2] L. Canny: A Computational Approach to Edge Detection, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 8 No. 1 679-698, (1986).

[3] P. L. Edward, O.R. Mitchell, M.L. Akey, A.P. Reeves: Subpixel Measurements Using a Moment Based Edge Operator, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11 No 12, 1293-1309 (1989).


[4] M. Heath, S. Sarkar, T. Sanocki, z and K. Bowyer, Comparison of Edge Detectors: A Methodology and Initial Study, Computer Vision And Image Understanding Vol. 69, No. 1, January, 38-54 (1998).


[5] H. J. Lin and J. P. Yeh: A Hybrid Optimization Strategy for Simplifying the Solutions of Support Vector Machines, Pattern Recognition Letters, 31, 563-571 (2010).


[6] D. Marr, E. Hildreth: Theory of Edge Detection, Proc. of Royal Society Landon, B(207): 187-217 (1980).

[7] R. Machuca: Finding Edges in Noisy Scenes, IEEE Trans. on Pattern Analysis and Machine Intelligence, 3: 103-111 (1981).


[8] J. P. Yeh, C. M. Chiang: Reducing the Solution of Support Vector Machines Using Simulated Annealing Algorithm, 2011 3rd International Conference on Machine Learning and Computing (ICMLC 2011) Singapore, February 26-28, (2011).

[9] V. N. Vapnik: Statistical Learning Theory, Wiley, New York, (1998).

[10] Q. Zhang, G. Shan, X. Duan, Z. Zhang: Parameters Optimization of Support Vector Machine based on Simulated Annealing and Genetic Algorithm, Proceedings of the 2009 IEEE International Conference on Robotics and Biomimetics, Guilin, China, December 19 -23, (2009).