Highly accurate optical flow method based on volumetric segmentation for 3D PIV

In this study, we present a new three-dimensional optical flow method based on volumetric segmentation for the velocity estimation of fluid flow. The proposed method uses a segmented smoothness term that is designed on the assumption that the particle velocity varies continuously in each segmented volume and discontinuously on the surfaces of the segmented volumes. Subsequently, the data term is proposed on the basis of the segmented volumes and the fluid mass conservation equation, which is derived from the Reynolds transport equation. In addition, the robust local level-set method is applied to segment the particle volume according to the velocity distribution of fluid flow. The proposed method is evaluated quantitatively on synthetic data and qualitatively on experimental data, and the velocity results are compared to the advanced 3D velocity estimation methods. The results indicate that the proposed method can obtain velocity fields with greater measurement accuracy for Tomo-PIV.

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Fluid flow and optical flow

Journal of Fluid Mechanics ◽

10.1017/s0022112008003273 ◽

2008 ◽

Vol 614 ◽

pp. 253-291 ◽

Cited By ~ 123

Author(s):

TIANSHU LIU ◽

LIXIN SHEN

Keyword(s):

Fluid Flow ◽

Optical Flow ◽

Lagrange Equation ◽

Fluid Velocity ◽

Flow Equation ◽

Flow Computation ◽

Flow Method ◽

Optical Flow Method ◽

Optical Flow Computation ◽

Particle Images

The connection between fluid flow and optical flow is explored in typical flow visualizations to provide a rational foundation for application of the optical flow method to image-based fluid velocity measurements. The projected-motion equations are derived, and the physics-based optical flow equation is given. In general, the optical flow is proportional to the path-averaged velocity of fluid or particles weighted with a relevant field quantity. The variational formulation and the corresponding Euler–Lagrange equation are given for optical flow computation. An error analysis for optical flow computation is provided, which is quantitatively examined by simulations on synthetic grid images. Direct comparisons between the optical flow method and the correlation-based method are made in simulations on synthetic particle images and experiments in a strongly excited turbulent jet.

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Divergence Compensatory Optical Flow Method for Blood Velocimetry

Journal of Biomechanical Engineering ◽

10.1115/1.4036484 ◽

2017 ◽

Vol 139 (6) ◽

Cited By ~ 5

Author(s):

Zifeng Yang ◽

Hongtao Yu ◽

George P. Huang ◽

Bryan Ludwig

Keyword(s):

Blood Flow ◽

Optical Flow ◽

Particle Image ◽

Vascular System ◽

Current Method ◽

Velocity Estimation ◽

Image Pair ◽

Flow Method ◽

Optical Flow Method

Detailed blood velocity map in the vascular system can be obtained by applying the optical flow method (OFM) in processing fluoroscopic digital subtracted catheter angiographic images; however, there are still challenges with the accuracy of this method. In the present study, a divergence compensatory optical flow method (DC-OFM), in which a nonzero divergence of velocity is assumed due to the finite resolution of the image, was explored and applied to the digital subtraction angiography (DSA) images of blood flow. The objective of this study is to examine the applicability and evaluate the accuracy of DC-OFM in assessing the blood flow velocity in vessels. First, an Oseen vortex flow was simulated on the standard particle image to generate an image pair. Then, the DC-OFM was applied on the particle image pair to recover the velocity field for validation. Second, DSA images of intracranial arteries were used to examine the accuracy of the current method. For each set of images, the first image is the in vivo DSA image, and the second image is generated by superimposing a given flow field. The recovered velocity map by DC-OFM agrees well with the exact velocity for both the particle images and the angiographic images. In comparison with the traditional OFM, the present method can provide more accurate velocity estimation. The accuracy of the velocity estimation can also be improved by implementing preprocess techniques including image intensification, Gaussian filtering, and “image-shift.”

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