期刊文献+

焊缝缺陷的计算机模式识别方法的研究 被引量:2

Study on the method of modeling identification of weld defect with computer
在线阅读 下载PDF
导出
摘要 在提取焊缝缺陷特征时 ,不仅考虑缺陷的几何形状 ,还考虑了灰度差及位置 ,并运用树形分类器和感知器进行分类 。 Not only the geometrical shape of defect,but also the gray level difference and position are considered in the character extracting of weld defect.By using the tree classifier and the perception to classify defect,the veracity of defect distinguishment is improved.
出处 《安庆师范学院学报(自然科学版)》 2001年第4期8-10,共3页 Journal of Anqing Teachers College(Natural Science Edition)
关键词 缺陷识别 感知器 焊缝缺陷 计算机模式识别 树形分类器 特征参数 weld defect distinguishment perception
  • 相关文献

参考文献3

二级参考文献3

共引文献22

同被引文献9

  • 1Wang G, Liao T W. Automatic identification of different types of welding defects in radiographic images [ J ]. NDT & E International, 2002, 35(8): 519-528.
  • 2Amartur S C, Piraino D and Takefuji Y. Optimization neural networks for the segmentation of magnetic resonance images [J ]. IEEE Transactions on Medical Imaging, 1992, 11(2) : 215-220.
  • 3Sanjay Gopal S, Berkman Sahiner, Heang-Ping Chan, et al. Neural network based segmentation using a Priori image models [ A ]. IEEE International Conference on Neural Networks-Conference Proceedings [C]. 1997, 4: 2455-2459.
  • 4Ghosh A, Pal N R and Pal S K. Neural netowrk, self-organization and object extraction[J]. IEEE Transactions on Fuzzy Systems, 1993, 1 (1) : 54-58.
  • 5Huang C L. Parallel image segmentation using modified hopfield model[J].Pattern Recognition Letters , 1993, 13(5): 345-353.
  • 6Cheng K S, Jzau Sheng Lin and Mao C W. The application of competitive hopfield neural network to medical image segmentation [J ]. IEEE Trans Medical Imaging, 1996, 15(4): 560-567.
  • 7叶芗芸,戚飞虎,蒋隽.基于选择性多分辨率Kohonen网络的自适应灰度图像分割方法[J].红外与毫米波学报,1998,17(1):48-53. 被引量:6
  • 8郑世才.关于缺陷影像识别的讨论[J].无损探伤,2002,26(1):5-8. 被引量:11
  • 9孙怡,孙洪雨,白鹏,王昱,田岩平.X射线焊缝图像中缺陷的实时检测方法[J].焊接学报,2004,25(2):115-118. 被引量:46

引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部