Crack image detection based on LS-SVM optimized by PSO

Author

Gao Yi ; Yang Yanxi

Author_Institution

Xi´an Univ. of Technol., Xi´an, China

fYear

2013

Firstpage

3602

Lastpage

3606

Abstract

A new crack image detection method based on Least Squares Support Vector Machines (LS-SVM) optimized by Particle Swarm Optimization (PSO) is proposed. Firstly the image intensity of neighborhood region of pixel is well estimated by LS-SVM, the gradient operator and zero crossings operator are obtained by LS-SVM based on Gaussian kernel. Then the method of edge detection based on the combination results of gradient and zero crossings is used to crack image detection. The parameters of LS-SVM are optimized by Chaos Particle Swarm Optimization Algorithm (CPSO) in order to achieve best edge detection performance. Finally, crack detecting experiments of glass bottles validate the effectiveness of the proposed method.

Keywords

Gaussian processes; chaos; edge detection; gradient methods; least squares approximations; particle swarm optimisation; support vector machines; CPSO; Gaussian kernel; LS-SVM; chaos particle swarm optimization; crack image detection; edge detection performance; glass bottles; gradient operator; image intensity; least squares support vector machines; neighborhood region; zero crossings operator; Abstracts; Chaos; Educational institutions; Image edge detection; Kernel; Particle swarm optimization; Support vector machines; Crack Detection; Gradient and zero crossing operators; Least squares support vector machines(LS-SVM); Particle Swarm Optimization (PSO);

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2013 32nd Chinese

Conference_Location

Xi´an

Type

conf

Filename

6640046