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Abstract
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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
Submitted: 2010 March, 26
Accepted: 2010 April, 16
Published: 2010 May, 10 |
Abstract:
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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.
Key words: Image processing, radiographic testing, support vector machine, welding defects, , ,
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Cite this Reference:
Xin Wang, Brian Stephen Wong and ChingSeong Tan, . Recognition of Welding Defects in Radiographic Images by Using Support Vector Machine Classifier. Research Journal of Applied Sciences, Engineering and Technology, (3): Page No: 295-301.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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