scholarly journals An integrated platform to facilitate the calculation, validation and visualization of optical flow velocities in biological images

2021 ◽  
Vol 18 (179) ◽  
pp. 20210248
Author(s):  
Xianbin Yong ◽  
Cheng-Kuang Huang ◽  
Chwee Teck Lim
Keyword(s):  
Optical Flow ◽  
Qualitative Assessment ◽  
Biological Images ◽  
Image Velocimetry ◽  
Integrated Platform ◽  
Matching Procedure ◽  
Simulated Images ◽  
Imaging Conditions ◽  
Artificial Tracers ◽  
Flow Velocities

Optical flow algorithms have seen poor adoption in the biological community compared with particle image velocimetry for quantifying cellular dynamics because of the lack of proper validation and an intuitive user interface. To address these challenges, we present OpFlowLab, an integrated platform that integrates our motion estimation workflow. Using routines in our workflow, we demonstrate that optical flow algorithms are more accurate than PIV in simulated images of the movement of nuclei. Qualitative assessment with actual nucleus images further supported this finding. Additionally, we show that refinement of the optical flow velocities is possible with a simple object-matching procedure, opening up the possibility of obtaining reasonable velocity estimates under less ideal imaging conditions. To visualize velocity fields, we employ artificial tracers to allow for the drawing of pathlines. Through the adoption of OpFlowLab, we are confident that optical flow algorithms will allow for the exploration of dynamic biological systems in greater accuracy and detail.

Wake states of a tethered cylinder

2007 ◽  
Vol 592 ◽  
pp. 1-21 ◽  
Author(s):  
J. CARBERRY ◽  
J. SHERIDAN
Keyword(s):  
Particle Image ◽  
Flow Fields ◽  
Low Flow ◽  
Response Data ◽  
Image Velocimetry ◽  
Mass Ratios ◽  
Induced Motion ◽  
The Mean ◽  
Low Amplitude ◽  
Flow Velocities

This paper describes an experimental investigation of a buoyant, m*<1, tethered cylinder which is free to move in an arc about its pivot points. The response of the cylinder, in particular its layover angle and flow-induced motion, is considered for a range of flow velocities and mass ratios. At pertinent parameters, the flow fields were also measured using particle image velocimetry (PIV). At lower mass ratios, 0.54≤m*≤0.72, two distinct states are observed, the low-amplitude and upper states. The transition from the low-amplitude state to the upper state is characterized by abrupt jumps in the amplitude of oscillation, the mean tether angle and the drag coefficient as well as distinct changes in the cylinder's wake. At higher mass ratios, the jump does not occur; however, as m* approaches unity at low flow velocities the cylinder's motion is more periodic than that observed at lower m*. The flow fields indicate that the low-amplitude state exhibits a 2S Kármán wake. The wake of the upper state has long shear layers extending well across the wake centreline, is not fully symmetric and is often consistent with either the 2P or P+S shedding modes. There is a collapse of the response data, in particular an excellent collapse of the mean layover angle, when the response parameters are plotted against the buoyancy Froude number, Frbuoyancy=U/((1-m*) gD)0.5. When the data collapses, the two states described above are clearly delineated.

scholarly journals The Optical Flow Method Research of Particle Image Velocimetry

2015 ◽  
Vol 99 ◽  
pp. 918-924 ◽  
Author(s):  
Wang Hongwei ◽  
Huang Zhan ◽  
Gong Jian ◽  
Xiong Hongliang
Keyword(s):  
Particle Image Velocimetry ◽  
Optical Flow ◽  
Particle Image ◽  
Flow Method ◽  
Optical Flow Method ◽  
Image Velocimetry

High Resolution Particle Image Velocimetry and CH-PLIF Measurements and Analysis of a Shear Layer Stabilized Flame

2015 ◽  
Vol 138 (3) ◽  
Author(s):  
C. W. Foley ◽  
I. Chterev ◽  
J. Seitzman ◽  
T. Lieuwen
Keyword(s):  
Particle Image Velocimetry ◽  
High Resolution ◽  
Shear Layer ◽  
Flow Velocity ◽  
Particle Image ◽  
Flame Stabilization ◽  
Flow Conditions ◽  
Local Flow ◽  
Image Velocimetry ◽  
Flow Velocities

Understanding the mechanisms and physics of flame stabilization and blowoff of premixed flames is critical toward the design of high velocity combustion devices. In the high bulk flow velocity situation typical of practical combustors, the flame anchors in shear layers where the local flow velocities are much lower. Within the shear layer, fluid strain deformation rates are very high and the flame can be subjected to significant stretch levels. The main goal of this work was to characterize the flow and stretch conditions that a premixed flame experiences in a practical combustor geometry and to compare these values to calculated extinction values. High resolution, simultaneous particle image velocimetry (PIV) and planar laser induced fluorescence of CH radicals (CH-PLIF) measurements are used to capture the flame edge and near-field stabilization region. When approaching lean limit extinction conditions, we note characteristic changes in the stretch and flow conditions experienced by the flame. Most notably, the flame becomes less critically stretched when fuel/air ratio is decreased. However, at these lean conditions, the flame is subject to higher mean flow velocities at the edge, suggesting less favorable flow conditions are present at the attachment point of the flame as blowoff is approached. These measurements suggest that blowoff of the flame from the shear layer is not directly stretch extinction induced, but rather the result of an imbalance between the speed of the flame edge and local tangential flow velocity.

scholarly journals Particle image velocimetry with optical flow

1998 ◽  
Vol 25 (3) ◽  
pp. 177-189 ◽  
Author(s):  
G. M. Quénot ◽  
J. Pakleza ◽  
T. A. Kowalewski
Keyword(s):  
Particle Image Velocimetry ◽  
Optical Flow ◽  
Particle Image ◽  
Image Velocimetry

FlowVeloTool: Measuring flow velocities in terrestrial and UAV image sequences automatically with PIV and PTV

2020 ◽  
Author(s):  
Anette Eltner ◽  
Jens Grundmann
Keyword(s):  
Flow Direction ◽  
Control Point ◽  
Region Of Interest ◽  
Software Tool ◽  
Image Sequences ◽  
Surface Flow ◽  
Area Of Interest ◽  
Image Velocimetry ◽  
Uav Image ◽  
Flow Velocities

&lt;p&gt;We introduce a Python based software tool to measure surface flow velocities and to estimate discharge eventually. Minimum needed input are image sequences, some camera parameters and object space information to scale the image measurements. Reference information can be provided either indirectly via ground control point measurements or directly providing camera pose parameters. To improve the reliability and density of velocity measurements the area of interest has to be masked for image velocimetry. This can either be performed with a binary mask file or considering a 3D point cloud, for instance retrieved with Structure from Motion (SfM) photogrammetry, describing the region of interest. The tracking task can be done with particle image velocimetry (PIV) considering small interrogation regions or using particle tracking velocimetry (PTV) and thus detecting and tracking features at the water surface. To improve the robustness of the tracking results, filtering can be applied that implements statistical information about the flow direction, flow steadiness and average velocities.&lt;/p&gt;&lt;p&gt;The FlowVeloTool has been tested with two different datasets; one at a gauging station and one at a natural river reach. Thereby, UAV and terrestrial data were acquired and processed. Velocities can be estimated with an accuracy of 0.01&amp;#160;m/s. If information about the river topography and bathymetry are available, as in our demonstration, discharge can be estimated with an error ranging from 5 to 31&amp;#160;% (Eltner et al. 2019). Besides these results we demonstrate further developments of the FlowVeloTool regarding filtering of tracking results, discharge estimation, and processing of time series. Furthermore, we illustrate that thermal data can be used, as well, with our tool to retrieve river surface velocities.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Eltner, A., Sardemann, H., and Grundmann, J.: Flow velocity and discharge measurement in rivers using terrestrial and UAV imagery, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-289, 2019.&lt;/p&gt;

Hybrid Optical-Flow-Cross-Correlation Method for Particle Image Velocimetry

2020 ◽  
Vol 142 (5) ◽  
Author(s):  
Tianshu Liu ◽  
David M. Salazar ◽  
Hassan Fagehi ◽  
Hassan Ghazwani ◽  
Javier Montefort ◽  
...  
Keyword(s):  
Particle Image Velocimetry ◽  
Optical Flow ◽  
Hybrid Method ◽  
Cross Correlation ◽  
Particle Image ◽  
Intensity Change ◽  
Flow Method ◽  
Optical Flow Method ◽  
Air Jet ◽  
Image Velocimetry

Abstract A hybrid method for particle image velocimetry (PIV) is developed to overcome the limitations of the optical flow method applied to PIV images with large displacements. The main elements of the hybrid method include a cross-correlation scheme for initial estimation, a shifting scheme for generating a shifted image, and an optical flow scheme for obtaining a refined high-resolution velocity field. In addition, a preprocessing scheme is used for correcting the illumination intensity change. The accuracy of the hybrid method is evaluated through simulations in a parametric space in comparison with the typical correlation methods and optical flow method. Further quantitative comparisons are made in PIV measurements in a circular air jet.

APPLYING FEATURE TRACKING TO PARTICLE IMAGE VELOCIMETRY

2003 ◽  
Vol 17 (04) ◽  
pp. 487-504 ◽  
Author(s):  
DMITRY CHETVERIKOV
Keyword(s):  
Particle Image Velocimetry ◽  
Optical Flow ◽  
Feature Tracking ◽  
Particle Image ◽  
Good Alternative ◽  
Flow Estimation ◽  
Tracking Algorithms ◽  
Alternative Approach ◽  
Image Velocimetry ◽  
Correlation Techniques

Particle Image Velocimetry (PIV) is a popular approach to flow visualization and measurement in hydro- and aerodynamic studies and applications. The fluid is seeded with particles that follow the flow and make it visible. Traditionally, correlation techniques have been used to estimate the displacements of the particles in a digital PIV sequence. These techniques are relatively time-consuming and noise-sensitive. Recently, an optical flow estimation technique developed in machine vision has been successfully used in Particle Image Velocimetry. Feature tracking is an alternative approach to motion estimation, whose application to PIV is proposed and studied in this paper. Two efficient feature tracking algorithms are customized and applied to PIV. The algorithmic solutions of the application are described. In particular, techniques for coherence filtering and interpolation of a velocity field are developed. To assess the proposed and the previous approaches, velocity fields obtained by the different methods are quantitatively compared for numerous synthetic and real PIV sequences. It is concluded that the tracking algorithms offer Particle Image Velocimetry a good alternative to both correlation and optical flow techniques.

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