Title :
A Novel Algorithm for Detecting Air Holes in Steel Pipe Welding Based on Hopfield Neural Network
Author :
Weixin, Gao ; Nan, Tang ; Xiangyang, Mu
Author_Institution :
Xian Shiyou Univ., Xian
fDate :
July 30 2007-Aug. 1 2007
Abstract :
The paper segment x-ray images of steel pipe welding to assess the quality of welding. Image segmentation is posed as an optimization problem, and is correlated with the energy function of the multistage Hopfield neural network. The algorithm for optimization and the principle of selecting coefficient are also given. The algorithm is easy to be programmed. As an application, we successfully segment some real industrial welding x-ray images.
Keywords :
Hopfield neural nets; X-ray imaging; image segmentation; object detection; optimisation; pipes; steel industry; welding; multistage Hopfield neural network; optimization problem; steel pipe welding air hole detection algorithm; x-ray image segmentation; Clustering algorithms; Gray-scale; Hopfield neural networks; Image segmentation; Neural networks; Steel; Welding; X-ray detection; X-ray detectors; X-ray imaging;
Conference_Titel :
Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-0-7695-2909-7
DOI :
10.1109/SNPD.2007.425
