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.