A new efficient SVM-based edge detection method

Pattern Recognition Letters

Volumn 25, Issue 10, 2004, Pages 1143-1154

  Zheng, Sheng ^a   Liu, Jian ^a   Tian, Jin Wen ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Edge detection; Gaussian radial basis function kernel; Gradient and zero crossing operators; Least squares support vector machine

Indexed keywords

ALGORITHMS; IMAGING TECHNIQUES; LEAST SQUARES APPROXIMATIONS; RADIAL BASIS FUNCTION NETWORKS;

LEAST SQUARES SUPPORT VECTOR MACHINE (LS-SVM); SOBEL AND CANNY DETECTORS;

EDGE DETECTION;

PATTERN RECOGNITION;

EID: 2942560335     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2004.03.009     Document Type: Article

References (37)

1. Abdou I.E. Pratt W.K. Quantitative design and evaluation of enhancement thresholding edge detectors Proc. IEEE 69 1979 753-763
2. Aizerman M.A. Braverman E.M. Rozono'er L.I. Theoretical foundations of the potential function method in pattern recognition learning Automat. Rem. Control 25 1964 821-837
3. Boser B.E. Guyon I.M. Vapnik V.N. A training algorithm for optimal margin classifiers Haussler D. 5th Annual ACM Workshop on COLT, Pittsburgh, PA 1992 144-152 ACM Press
4. Burges C.J.C. A tutorial on support vector machines for pattern recognition Knowl. Discovery Data Min. 2 2 1998 121-167
5. Canny J. A computational approach to edge detection IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8 1986 679-698
6. Cortes C. Vapnik V. Support vector networks Mach. Learn. 20 1995 273-297
7. Hansen F.R. Elliot H. Image segmentation using simple Markov field models Comput. Graphics Image Process 20 1982 101-132
8. Haralick R.M. Digital step edges from zero crossing second directional derivatives IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6 1984 58-68
9. Haralick R.M. Watson L. A facet model for image data Comput. Graphics Image Process. 15 1981 113-129
10. Heath M.D. Sarkar S. Sanocki T. Bowyer K.W. A robust visual method for assessing the relative performance of edge-detection algorithms IEEE Trans. Pattern Anal. Mach. Intell. 19 1997 1338-1359
11. Hertz L. Schafer R.W. Multilevel thresholding using edge matching Comput. Vision Graphics Image Process 44 1988 279-295
12. Hou Z.J. Koh T.S. Robust edge detection Pattern Recognit. 36 9 2003 2083-2091
13. Hou Z.J. Wei G.W. A new approach to edge detection Pattern Recognit. 35 7 2002 1559-1570
14. Qiang Ji Haralick Robert M. Efficient facet edge detection and quantitative performance evaluation Pattern Recognit. 35 3 2002 689-700
15. Karush, W., 1939. Minima of functions of several variables with inequalities as side constraints. Masters thesis. Department of Mathematics, University of Chicago
16. Konishi S. Yuille A.L. Coughlan J.M. A statistical approach to multi-scale edge detection Image Vision Comput. 21 1 2003 37-48
17. Konishi S. Yuille A.L. Coughlan J.M. Song Chun Zhu Statistical edge detection: Learning and evaluating edge cues IEEE Trans. Pattern Anal. Mach. Intell. 25 1 2003 57-74
18. Kuhn H.W. Tucker A.W. Nonlinear programming Proc. 2nd Berkeley Symposium on Mathematical Statistics and Probabilistics, Berkeley 1951 481-492 University of California Press
19. Manjunath B.S. Chellappa R. A unified approach to boundary perception: Edges, textures and illusory contours IEEE Trans. Neural Networks 4 1993 96-108
20. Marr D. Hildreth E. Theory of edge detection Proc. Roy. Soc. B 207 1980 187-217
21. Mehrotra R. Zhan S. Computational approach to zero-crossing-based two-dimensional edge detection Graphical Models and Image Process. 58 1 1996 1-17
22. Mercer J. Functions of positive and negative type and their connection with the theory of integral equations Philos. Trans. Roy. Soc. London, A 209 1909 415-446
23. Micchelli C.A. Interpolation of scattered data: Distance matrices and conditionally positive definite functions Constr. Approx. 2 1986 11-22
24. Müller K.R. Mika S. Rätsch G. Tsuda K. Schölkopf B. An introduction to kernel-based learning algorithms IEEE Trans. Neural Networks 12 2 2001 181-201
25. Nahi N.E. Assefi T. Bayesian recursive image estimation IEEE Trans. Comput. 7 1972 734-738
26. Peli T. Malah D. A study of edge detection algorithms Comput. Graphics Image Process 20 1982 1-21
27. Sanger T.D. Optimal unsupervised learning in a single layer feed forward neural network Neural Networks 2 1989 459-473
28. Sarkar S. Boyer K.L. On optimal infinite impulse response edge detection filters IEEE Trans. Pattern Anal. Mach. Intell. PAMI-3 1991 1154-1171
29. Sobel I. Neighborhood coding of binary images for fast contour following and general array binary processing Comput. Graphics Image Process. 8 1978 127-135
30. Stern D. Kurz L. Edge detection in correlated noise using Latin squares models Pattern Recognit. 21 1988 119-129
31. Suykens J.A.K. Vandewalle J. Least squares support vector machine classifiers Neural Process. Lett. 9 3 1999 293-300
32. Suykens J.A.K. Van Gestel T. De Brabanter J. De Moor B. Vandewalle J. Least Squares Support Vector Machines 2002 World Scientific Singapore
33. Van Gestel, T., Suykens J., Baestaens D., Lambrechts A., Lanckriet G., Vandaele B., De Moor B., Vandewalle J., 2001. Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Transactions on Neural Networks, special issue on Neural Networks in Financial Engineering 12(4), 809-821
34. Vapnik V. The Nature of Statistical Learning Theory 1995 Springer-Verlag New York
35. Vapnik V. Statistical Learning Theory 1998 John Wiley New York
36. Vapnik V. The support vector method of function estimation Suykens J.A.K. Vandewalle J. Nonlinear Modeling: Advanced Black-Box Techniques 1998 55-85 Kluwer Academic Publishers Boston
37. Vapnik V. Golowich S. Smola A. Support vector method for function approximation, regression estimation, and signal processing Mozer M. Jordan M. Petsche T. The Advances in Neural Information Processing Systems 1997 281-287 Cambridge, MA MIT Press