Home            Contact us            FAQs
    
      Journal Home      |      Aim & Scope     |     Author(s) Information      |      Editorial Board      |      MSP Download Statistics

     Research Journal of Applied Sciences, Engineering and Technology

    Abstract
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 
Submitted: 2010 March, 26
Accepted: 2010 April, 16
Published: 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.

Key words:  Image processing, radiographic testing, support vector machine, welding defects, , ,
Abstract PDF HTML
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.
ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
Submit Manuscript
   Information
   Sales & Services
Home   |  Contact us   |  About us   |  Privacy Policy
Copyright © 2024. MAXWELL Scientific Publication Corp., All rights reserved