Paper Info
Title
Improved Edge Detection Based on LS-SVM
Abstract
In this paper, the image is filtered with the adaptive multi-scale Gauss filter based on intensity image's mean square value histogram, that is, with the statistical information of the image itself. And the intensity surface of the image filtered by the adaptive multi-scale Gauss filter for the neighborhood of every pixel is well-fitted by the Least Squares Support Vector Machine (LS-SVM), the gradient and the zero-crossing operators are deduced from the LS-SVM with the Radial Basis Function (RBF) kernel. And then the decision is made as to whether a pixel is an edge or not based on the combination results of the gradient and the zero-crossings. Computer experiments are carried out. Compared with the LS-SVM with RBF kernel function by using single scale parameter Gauss filter to suppress the noise, the experimental results demonstrate the proposed algorithm is efficient, especially, when the SNR is lower.
Year
DOI
Venue
2008
10.1109/ICNC.2008.201
ICNC
Keywords
Field
DocType
intensity surface,improved edge detection,radial basis function,adaptive multi-scale gauss,squares support,vector machine,rbf kernel function,intensity image,computer experiment,combination result,single scale parameter gauss,noise,pixel,adaptive filters,edge detection,kernel function,support vector machines,kernel,least squares support vector machine
Radial basis function kernel,Edge detection,Artificial intelligence,Adaptive filter,Gaussian filter,Mathematical optimization,Pattern recognition,Least squares support vector machine,Kernel adaptive filter,Pixel,Mathematics,Recursive least squares filter,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
8
Authors
2
Name
Order
Citations
PageRank
Wei Guo1442146.38
Zhenhong Jia22915.13