DocumentCode
498991
Title
Notice of Violation of IEEE Publication Principles
A support vector machine approach for edge detection in noisy images
Author
Zhang, Jian-min ; Li, Lei
Author_Institution
Coll. of Autom., Nanjing Univ. of Posts & Telecommun., Nanjing, China
Volume
2
fYear
2009
fDate
12-15 July 2009
Firstpage
965
Lastpage
969
Abstract
Notice of Violation of IEEE Publication Principles
" A Support Vector Machine Approach for Edge Detection in Noisy Images,"
by Jian-Min Zhang and Lei Li
in the Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, 12-15 July 2009
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.
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.
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:
"Edge detection in noisy images using the Support Vector Machines"
by H. G??mez Moreno, S. Maldonado Basc??n, F. L??pez Ferreras,
in Lecture Notes in Computer Science. Vol. 2084, pp. 685-692, 2001. ?? Springer-Verlag.
"A new and improved edge detector using the Support Vector Machines".
by H. G??mez Moreno, S. Maldonado Basc??n, F. L??pez Ferreras, P. Gil Jim??nez,
in Advances in Systems Engineering, Signal Processing and Communications, pp. 239-243.
Ed: N. Mastorakis. Editorial WSES Press, USA, 2002. ISBN:960-8052-69-6
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.
" A Support Vector Machine Approach for Edge Detection in Noisy Images,"
by Jian-Min Zhang and Lei Li
in the Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, 12-15 July 2009
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.
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.
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:
"Edge detection in noisy images using the Support Vector Machines"
by H. G??mez Moreno, S. Maldonado Basc??n, F. L??pez Ferreras,
in Lecture Notes in Computer Science. Vol. 2084, pp. 685-692, 2001. ?? Springer-Verlag.
"A new and improved edge detector using the Support Vector Machines".
by H. G??mez Moreno, S. Maldonado Basc??n, F. L??pez Ferreras, P. Gil Jim??nez,
in Advances in Systems Engineering, Signal Processing and Communications, pp. 239-243.
Ed: N. Mastorakis. Editorial WSES Press, USA, 2002. ISBN:960-8052-69-6
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.
Keywords
edge detection; filtering theory; image classification; regression analysis; support vector machines; SVM classification; SVM regression networks filter; edge detection; noisy images; support vector machine; Edge detection; Image denoising; Learning methods; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location
Hebei
Print_ISBN
978-1-4244-3702-3
Electronic_ISBN
978-1-4244-3703-0
Type
conf
DOI
10.1109/ICMLC.2009.5212422
Filename
5212422
Link To Document