A new efficient SVM-based edge detection method Author: Sheng Zheng*, Jian Liu, Jin Wen Tian

A new efficient SVM-based edge detection method

Author: Sheng Zheng*, Jian Liu, Jin Wen TianRepresentative: Toan Minh HoangStudentId: 2014126929ContentsIntroductionSVM and LS-SVMProposed theory and algorithmResult and discussionReferences5/29/20152IntroductionEdge detectionConsidered as a key issue in image processing, computer vision, and pattern recognition

Why SVM?Maps input data into a high dimensional feature space where it may become linearly separable.Was designed to minimize structural risk => often performs better than earlier methodsUsually less vulnerable to overfitting problem.

5/29/20153ContentsIntroductionSVM and LS-SVMProposed theory and algorithmResult and discussionReferences5/29/20154SVM and LS-SVM2.1. SVM5/29/20155SVM and LS-SVM2.2. LS-SVM (1)5/29/20156SVM and LS-SVM2.2. LS-SVM (2)5/29/20157ContentsIntroductionSVM and LS-SVMProposed theory and algorithmResult and discussionReferences5/29/20158Proposed theory and algorithm3.1. Gray level intensity surface5/29/20159Proposed theory and algorithm3.1. Gray level intensity surface(2)5/29/201510Proposed theory and algorithm3.2. Derivatives of the intensity surface5/29/201511Proposed theory and algorithm3.2. Derivatives of the intensity surface(2)5/29/201512ContentsIntroductionSVM and LS-SVMProposed theory and algorithmResult and discussionReferences5/29/201513Result and discussionAbility of edge extraction with data smoothing

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Result and discussion

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ContentsIntroductionSVM and LS-SVMProposed theory and algorithmResult and discussionReferences5/29/201517ReferencesBurges, C.J.C., 1998. A tutorial on support vector machines for pattern recognition. Knowl. Discovery Data Min. 2 (2), 121-167.Canny, J., 1986. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8, 679-698Cortes, C., Vapnik, V., 1995. Support vector networks. Mach. Learn. 20, 273-297.Hou, Z.J.m Koh, T.S., 2003. Robust edge detection. Pattern Recognit. 35(3), 689-700.Kuhn, H.W., Tucker, A.W., 1951. Nonlinear programming. In Proc. 2nd Berkley Symposium on Mathematical Statistics and Probabilistics, Berkeley. University of California Press, pp.481-492.Peli, T.m Malah, D., 1982. A sutdy of edge detection algorithms. Comput. Graphics Image Process 20, 1-21.Suykens, J.A.K., Vandewalle, J., 1000. Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293-300.Vapnik, V., 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York.Vapnik, V., 1998a. Statistical Learning Theory. John Willey, New York.Vapnik, V., 1998b. The support vector method of function estimation. In: Suykens, J.A.K., Vandewalle, J. (Eds.). Kluwer Academic Publishers, Boston, pp.55-85.

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