H. I. Shafeek, E. S. Gadelmawla, A. A. Abdel-Shafy and I. M. Elewa, “Automatic Inspection of Gas Pipeline Welding Defects Using an Expert Vision System,” Nondestructive Testing and Evaluation: An International Journal, Vol. 37, No. 4, 2004, pp. 301-307.

has been cited by the following article:

• TITLE: Higher-Order Statistics for Automatic Weld Defect Detection

  AUTHORS: Sara Saber, Gamal I. Selim

  KEYWORDS: High Order Statistics; Defect Detection; Radiographic Images; Non-Destructive Testing

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.6 No.5, May 20, 2013

  ABSTRACT: 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.