Notice of Violation of IEEE Publication Principles - A support vector machine approach for edge detection in noisy images

Abstract

Notice of Violation of IEEE Publication Principles<br><br>" A Support Vector Machine Approach for Edge Detection in Noisy Images,"<br>by Jian-Min Zhang and Lei Li<br>in the Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, 12-15 July 2009<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br>This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br>Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:<br><br>"Edge detection in noisy images using the Support Vector Machines"<br>by H. Gómez Moreno, S. Maldonado Bascón, F. López Ferreras,<br>in Lecture Notes in Computer Science. Vol. 2084, pp. 685-692, 2001. © Springer-Verlag.<br><br> "A new and improved edge detector using the Support Vector Machines".<br>by H. Gómez Moreno, S. Maldonado Bascón, F. López Ferreras, P. Gil Jiménez,<br>in Advances in Systems Engineering, Signal Processing and Communications, pp. 239-243.<br>Ed: N. Mastorakis. Editorial WSES Press, USA, 2002. ISBN:960-8052-69-6<br><br> <br/> An innovative edge detection method, using the support vector machine, is presented in this paper. This method shows how to use the SVM to detect edge in an efficient way. The noisy images are processed in two ways in this paper, first reducing the noise by using the SVM regression networks filter and then performing the classification using the SVM classification. The experimental results indicate that this method is near equal to the Canny in the performance and is fast in the speed when the images are affected by impulsive noise.