• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied optics >Neural-network method applied to the stereo image correspondence problem in three-component particle image velocimetry
【24h】

Neural-network method applied to the stereo image correspondence problem in three-component particle image velocimetry

机译：神经网络方法应用于三分量粒子图像测速中的立体图像对应问题

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The successful application of a recurrent neural network of the Hopfield type to the solution of the stereo image-pair reconciliation problem in stereoscopic particle image velocimetry (PIV) in the tracking mode is described. The results of applying the network to both virtual-flow and physical-flow PIV data sets are presented, and the usefulness of this novel approach to PIV stereo image analysis is demonstrated. A partner-particle image-pair density (PPID) parameter is defined as the average number of potential particle image-pair candidates in the search window in the second view corresponding to a single image pair in the first view. A quantitative assessment of the performance of the method is then made from groups of 100 synthetic flow images at various values of the PPID. The successful pairing of complementary image points is shown to vary from 100% at a PPID of 1 and to remain greater than 97% successful for PPID's up to 5. The application of the method to a hydraulic flow is also described, with in-line stereo images presented, and the application of the neural-matching method is demonstrated for a typical data set. (C) 1998 Optical Society of America. [References: 12]
机译：描述了在跟踪模式下，Hopfield型递归神经网络在立体粒子图像测速（PIV）中解决立体图像对和解问题的成功应用。给出了将网络应用于虚拟流和物理流PIV数据集的结果，并证明了这种新颖的方法进行PIV立体图像分析的有用性。伙伴粒子图像对密度（PPID）参数定义为第二视图中与第一视图中的单个图像对相对应的搜索窗口中潜在粒子图像对候选对象的平均数量。然后从100个不同PPID值的合成流图像的组中对该方法的性能进行定量评估。互补图像点的成功配对显示为PPID为1时100％变化，而PPID高达5时成功大于97％。该方法在液压流中的应用也进行了在线描述。展示了立体图像，并针对典型数据集演示了神经匹配方法的应用。 （C）1998年美国眼镜学会。 [参考：12]

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号