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2010 (Vol. 2, Issue: 3)
Article Information:

Recognition of Welding Defects in Radiographic Images by Using Support Vector Machine Classifier

Xin Wang, Brian Stephen Wong and ChingSeong Tan
Corresponding Author:  Xin Wang 

Key words:  Image processing, radiographic testing, support vector machine, welding defects, , ,
Vol. 2 , (3): Page No: 295-301
Submitted Accepted Published
2010 March, 26 2010 April, 16 2010 May, 10
Abstract:

Radiographic testing method is often used for detecting defects as a non-destructive testing method. In this paper, an automatic computer-aided detection system based on Support Vector Machine (SVM) was implemented to detect welding defects in radiographic images. After extracting potential defects, two group features: texture features and morphological features are extracted. Afterwards SVM criteria and receiver operating characteristic curves are used to select features. Then Top 16 best features are used as inputs to a designed SVM classifier. The behavior of the proposed classification method is compared with various other classification techniques: k-means, linear discriminant, k-nearest neighbor classifiers and feed forward neural network. The results show the efficiency proposed method based on the support vector machine.
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  Cite this Reference:
Xin Wang, Brian Stephen Wong and ChingSeong Tan, 2010. Recognition of Welding Defects in Radiographic Images by Using Support Vector Machine Classifier.  Research Journal of Applied Sciences, Engineering and Technology, 2(3): Page No: 295-301.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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