Higher-Order Statistics for Automatic Weld Defect Detection


Image processing and image analysis are the main aspects for obtaining information from digital image owing to the fact that this techniques give the desired details in most of the applications generally and Non-Destructive testing specifically. This paper presents a proposed method for the automatic detection of weld defects in radiographic images. Firstly, the radiographic images were enhanced using adaptive histogram equalization and are filtered using mean and wiener filters. Secondly, the welding area is selected from the radiography image. Thirdly, the Cepstral features are extracted from the Higher-Order Spectra (Bispectrum and Trispectrum). Finally, neural networks are used for feature matching. The proposed method is tested using 100 radiographic images in the presence of noise and image blurring. Results show that in spite of time consumption, the proposed method yields best results for the automatic detection of weld defects in radiography images when the features were extracted from the Trispectrum of the image.

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S. Saber and G. Selim, "Higher-Order Statistics for Automatic Weld Defect Detection," Journal of Software Engineering and Applications, Vol. 6 No. 5, 2013, pp. 251-258. doi: 10.4236/jsea.2013.65031.

Conflicts of Interest

The authors declare no conflicts of interest.


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