• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Development of a Vision-Based Particle Tracking Velocimetry Method and Post-Processing of Scattered Velocity Data.
【24h】

Development of a Vision-Based Particle Tracking Velocimetry Method and Post-Processing of Scattered Velocity Data.

机译：基于视觉的粒子跟踪测速方法的开发和散射速度数据的后处理。

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

In this thesis, a new vision-based hybrid particle tracking velocimetry (VB-PTV) technique is described and methods of processing randomly scattered velocity data investigated. The VB-PTV technique uses a feature matching method from computer vision theory which relies on the principles of proximity, similarity, and exclusion, meaning that it seeks to match one feature to one feature in subsequent images, and it favors matches which are close to one another and "look" similar. By constructing a matrix which takes these principles into account and performing singular value decomposition, a straightforward method of matching is developed which can give accurate matching results in a wide variety of flows. PIV velocity information is used to provide guidance to the matching algorithm. In addition, matches are made iteratively and validated by an outlier detection scheme. When this method is tested on synthetic images it results in matches which are typically reliable more than 98% of the time. A simple modification to the principle of proximity is introduced which reduces the PTV method's errors in highly shearing flow, as well as improving performance in general for various flow types.;Finally, a natural neighbor-based interpolation technique is investigated for use in estimating flow derivatives using scattered velocity data. This interpolation method is compared with other existing techniques in terms of accuracy, sensitivity to noise, computational efficiency, and spatial resolution. It is found that the natural neighbor interpolation is less accurate than RBF and kriging interpolation methods, and more sensitive to noise, despite the use of a denoising technique.
机译：本文介绍了一种新的基于视觉的混合粒子跟踪测速技术（VB-PTV），并研究了随机散射速度数据的处理方法。 VB-PTV技术使用了计算机视觉理论中的一种特征匹配方法，该方法依赖于邻近性，相似性和排除性原理，这意味着它试图在后续图像中将一个特征与一个特征进行匹配，并且倾向于接近彼此和“看起来”相似。通过构造一个考虑了这些原理的矩阵并执行奇异值分解，可以开发出一种简单的匹配方法，该方法可以在各种流程中给出精确的匹配结果。 PIV速度信息用于为匹配算法提供指导。此外，反复进行匹配并通过异常值检测方案进行验证。在合成图像上测试此方法时，其匹配结果通常在98％以上的时间内都是可靠的。引入了对邻近原理的简单修改，以减少PTV方法在高剪切流中的误差，并总体上提高了各种流类型的性能。最后，研究了一种基于自然邻域的插值技术以估算流使用分散的速度数据导数。在准确性，对噪声的敏感性，计算效率和空间分辨率方面，将该插值方法与其他现有技术进行了比较。结果发现，尽管使用了降噪技术，但自然邻域内插比RBF和kriging内插方法精度低，并且对噪声更敏感。

著录项

  • 作者

    Paul, Micah Philip.;

  • 作者单位

    University of Washington.;

  • 授予单位 University of Washington.;
  • 学科 Engineering Aerospace.
  • 学位 M.S.A.A.
  • 年度 2012
  • 页码 72 p.
  • 总页数 72
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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