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A Fluid Motion Estimator for Schlieren Image Velocimetry

机译:用于Schlieren图像测速的流体运动估计器

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摘要

In this paper, we address the problem of estimating the motion of fluid flows that are visualized through a Schlieren system. Such a system is well known in fluid mechanics as it enables the visualization of unseeded flows. As the resulting images exhibit very low photometric contrasts, classical motion estimation methods based on the brightness consistency assumption (correlation-based approaches, optical flow methods) are completely inefficient. This work aims at proposing a sound energy based estimator dedicated to these particular images. The energy function to be minimized is composed of (a) a novel data term describing the fact that the observed luminance is linked to the gradient of the fluid density and (b) a specific div curl regularization term. The relevance of our estimator is demonstrated on real-world sequences.
机译:在本文中,我们解决了估计通过Schlieren系统可视化的流体流动运动的问题。这样的系统在流体力学中是众所周知的,因为它能够可视化非种子流。由于所得图像显示出非常低的光度对比度,因此基于亮度一致性假设的经典运动估计方法(基于相关的方法,光流方法)完全无效。这项工作旨在提出一种专门针对这些特定图像的基于声能的估计器。待最小化的能量函数由(a)描述观测亮度与流体密度梯度相关的事实的新数据项和(b)特定div卷曲正则化项组成。我们的估算器的相关性在现实世界的序列上得到了证明。

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