scholarly journals A robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded

2021 ◽  
Vol 33 (12) ◽  
pp. 127125
Author(s):  
Qi Gao ◽  
Hongtao Lin ◽  
Han Tu ◽  
Haoran Zhu ◽  
Runjie Wei ◽  
...  
Keyword(s):  
Particle Image Velocimetry ◽  
Cross Correlation ◽  
Particle Image ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Image Velocimetry ◽  
Single Pixel

Particle Image Velocimetry: Automatic Fringe Analysis By Cross-Correlation

1988 ◽  
Vol 27 (3) ◽  
Author(s):  
Atsushi Kirita ◽  
Christopher J. D. Pickering ◽  
Neil A. Halliwell
Keyword(s):  
Particle Image Velocimetry ◽  
Cross Correlation ◽  
Particle Image ◽  
Fringe Analysis ◽  
Image Velocimetry

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.

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