scholarly journals Hybrid Adaptive Wavelet-Based Optical Flow Algorithm for Background Oriented Schlieren (BOS) Experiments

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xin-yu Zhang ◽  
Li-Ming Wang ◽  
Bin Liu ◽  
Yue Luo ◽  
Xing-cheng Han
Keyword(s):  
Flow Field ◽  
Optical Flow ◽  
Measurement Technology ◽  
Refractive Index Gradient ◽  
Adaptive Wavelet ◽  
Flow Algorithm ◽  
Current Algorithm ◽  
Index Gradient ◽  
Background Oriented Schlieren ◽  
Flow Field Measurement

Quantitative analysis of the flow field is an effective method to study hydrodynamics. As a flow field measurement technology, the Background Oriented Schlieren (BOS) is widely used. However, it is difficult to measure the complex transparent flow field (flow field with large refractive index gradient) using the BOS experiment. In order to overcome this difficulty and improve the accuracy of the BOS experiment, this paper presents a hybrid adaptive wavelet-based optical flow algorithm for the BOS. The current algorithm is a combination of the traditional optical flow algorithm and the wavelet-based optical flow algorithm. By adding the initial value constraints, the adaptive scale constraints, and the adaptive regularization constraints, the algorithm can effectively overcome the above-mentioned difficulty and also improve its accuracy. To further illustrate the feasibility of the proposed method, this paper uses the simulation data, the data of the DNS datasets, and the data of the BOS experiment to verify the performance of the algorithm. The experiment of comparing the proposed algorithm with the existing ones is carried out on the DNS datasets and the data of the BOS experiment. Finally, the proposed method is verified by a practical BOS experiment. The results show that the proposed algorithm can effectively improve the measurement accuracy of displacements.

Accuracy Verification of 2-Dimensional Velocity Field Derived From Optical Flow With Respect to PIV Flow Field Measurement

2020 ◽  
Author(s):  
Mingli Cui ◽  
Zhe Sun ◽  
Min Xu ◽  
David Hung ◽  
Xuesong Li
Keyword(s):  
Flow Field ◽  
Optical Flow ◽  
Field Measurement ◽  
Ambient Air ◽  
Fuel Spray ◽  
Measurement Technology ◽  
Air Velocity ◽  
Tracer Particles ◽  
Image Velocimetry ◽  
Flow Field Measurement

Abstract As a mature flow field measurement technology, particle image velocimetry (PIV) has been widely used to calculate the instantaneous motion of particles by identifying and comparing the positions of tracer particles between successive image frames. For example, the ambient air velocity distributions around fuel spray can be measured using LIF-PIV However, due to the need to accurately identify the tracer particles, PIV is limited in its applications, such as measuring the flow field in turbid media. Optical flow calculates apparent velocities of movement of brightness patterns in an image. Different from PIV, optical flow segments images into regions that corresponds to different objects without requirement of tracer particles. Since it is based on brightness patterns, optical flow may be widely used in measuring flow field in the area of fuel spray and in-cylinder combustion. To check the accuracy and robustness of optical flow, this study will calculate the 2-dimensional velocity fields of the same PIV image sequences using iterative optical flow and PIV respectively. Using PIV results as criterions, precision and accuracy of optical flow are studied.

Application of L-K Optical Flow Algorithm Mixed with Pyramid on Calculating Water Surface Flow Field

2013 ◽  
Vol 333-335 ◽  
pp. 897-903 ◽  
Author(s):  
Zhao Hui Han ◽  
Yan Feng Wang
Keyword(s):  
Velocity Field ◽  
Flow Field ◽  
Optical Flow ◽  
Water Surface ◽  
Image Sequence ◽  
Surface Flow ◽  
Aperture Problem ◽  
Wind Speeds ◽  
Gaussian Pyramid ◽  
Flow Algorithm

A classical Lucas-Kanade optical flow algorithm was used to analysis the IR Image sequence of the wind-driven surface in this paper. Gaussian pyramid representation was introduced to retain both detail components and veracity for velocity field when considering the aperture problem and robustness. Three layers of pyramid for L-K optical flow is the best comparing with other layers (from one to four) in property. L-K optical flow algorithm mixed with pyramid representation shown an qualified power on calculating water surface flow field, demonstrated by optical flow fields on different wind speeds ( from 3m/s to 6m/s).

Motion correction in PET/CT

2005 ◽  
Vol 44 (S 01) ◽  
pp. S46-S50 ◽  
Author(s):  
M. Dawood ◽  
N. Lang ◽  
F. Büther ◽  
M. Schäfers ◽  
O. Schober ◽  
...  
Keyword(s):  
Optical Flow ◽  
Motion Correction ◽  
Motion Vector ◽  
Emission Tomography ◽  
Cardiac Pet ◽  
Novel Approach ◽  
Positron Emission ◽  
Pet Ct ◽  
Flow Algorithm ◽  
Motion Vector Field

Summary:Motion in PET/CT leads to artifacts in the reconstructed PET images due to the different acquisition times of positron emission tomography and computed tomography. The effect of motion on cardiac PET/CT images is evaluated in this study and a novel approach for motion correction based on optical flow methods is outlined. The Lukas-Kanade optical flow algorithm is used to calculate the motion vector field on both simulated phantom data as well as measured human PET data. The motion of the myocardium is corrected by non-linear registration techniques and results are compared to uncorrected images.

scholarly journals Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2407
Author(s):  
Hojun You ◽  
Dongsu Kim
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Optical Flow ◽  
Hyperspectral Imaging ◽  
Spatial Resolution ◽  
Template Matching ◽  
Hyperspectral Images ◽  
Spatial Accuracy ◽  
Flow Algorithm ◽  
Fluvial Remote Sensing

Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red–green–blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching.

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