This paper presents a proposed method for the detection of welds defects from radiographic images. Firstly, the radiographic images were enhanced using Adaptive Histogram Equalization and were filtered using Mean and Wiener filters. Secondly, the welding area was selected from the radiography image. Thirdly, the images were converted to signals then the features were extracted from the Bispectrum of these signals. Finally, neural networks were used for training and testing the proposed method. The proposed model was tested on 100 radiography images in the presence of noise and image blurring. Results show that the proposed model yields best results for the detection of weld defects in radiography images when using the Bispectrum method estimated by Autoregressive moving average (ARMA) method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.