Wavelets and Fluid Motion Estimation
Wavelets and Fluid Motion Estimation
复制标题
小波和流体运动估计
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
P. Dérian
中科院分区：
文献类型：
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作者：
P. Dérian
这项工作福尔斯属于设计适合于流体流动的特性的测量工具的一般问题。数字成像的发展，结合实验流体动力学中常用的可视化技术，使得能够从图像序列中提取表观流动运动，这要归功于计算机视觉方法。我们的目标是提出一种新的“光流”算法，致力于流体流动的多尺度运动估计，使用小波表示的未知运动场。该小波公式引入了一个多尺度框架，方便地适应光流估计和湍流运动场的表示。它还能够设计无发散的基座，从而尊重流体动力学给出的约束。提出了几种正则化方案;最简单的包括在细尺度截断的基础，而最复杂的建立高阶计划的小波基的连接系数。建议的方法首先在合成图像上进行评估，然后在特征流体流动的实际实验图像上进行评估。结果进行比较，通常的“互相关”，突出的优点和局限性的基于小波的估计。
This work falls within the general problematic of designing measurement tools adapted to the specificities of fluid flows. The development of digital imaging, combined with visualization techniques commonly employed in experimental fluid dynamics, enables to extract the apparent flow motion from image sequences, thanks to computer vision methods. The objective is to propose a novel "optical flow" algorithm dedicated to the multiscale motion estimation of fluid flows, using a wavelet representation of the unknown motion field. This wavelet formulation introduces a multiscale framework, conveniently adapted both to the optical flow estimation and to the representation of turbulent motion fields. It enables as well to design divergence-free bases, thereby respecting a constraint given by fluid dynamics. Several regularization schemes are proposed; the simplest consists in truncating the basis at fine scales, while the most complex builds high-order schemes from the connection coefficients of the wavelet basis. Proposed methods are evaluated on synthetic images in the first place, then on actual experimental images of characteristic fluid flows. Results are compared to those given by the usual "cross-correlations", highlighting the advantages and limits of the wavelet-based estimator.
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DOI：
10.1007/978-1-4939-7647-8_1
发表时间：
2018
期刊：
Neuromethods
影响因子：
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作者：
Joshi,AnandA
通讯作者：
Joshi,AnandA