基于Hopfield神经网络的钢管焊接气孔检测新算法

Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007) Pub Date : 2007-07-30 DOI:10.1109/SNPD.2007.66

Weixin Gao, Tang Nan, Xiangyang Mu

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引用次数: 3

摘要

采用纸段x射线图像对钢管焊接质量进行评定。将图像分割作为一个优化问题，与多级Hopfield神经网络的能量函数相关联。给出了优化算法和选择系数的原则。该算法易于编程。作为应用，我们成功地分割了一些真实的工业焊接x射线图像。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Novel Algorithm for Detecting Air Holes in Steel Pipe Welding Based on Hopfield Neural Network

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.

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Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007)

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