scholarly journals On the Texture Angle Detection Used in Space-Time Image Velocimetry (STIV)

2017 ◽  
Vol 53 (12) ◽  
pp. 10908-10914 ◽  
Author(s):  
Ryota Tsubaki
Keyword(s):  
Space Time ◽  
Image Velocimetry ◽  
Time Image

scholarly journals Wavenumber-frequency analysis of river surface texture to improve accuracy of image-based velocimetry

2018 ◽  
Vol 40 ◽  
pp. 06012
Author(s):  
Kojiro Tani ◽  
Ichiro Fujita
Keyword(s):  
Image Analysis ◽  
Frequency Analysis ◽  
Surface Texture ◽  
Space Time ◽  
Surface Flow ◽  
Flow Measurements ◽  
Image Velocimetry ◽  
Wave Effects ◽  
Floating Objects ◽  
Time Image

In the unseeded image-based techniques for river surface flow measurements, advection speed of surface textures composed of surface ripples or floating objects is measured by image analysis. However, the methods would yield erroneous information when the surface texture is affected by gravity waves propagating in all directions. In order to improve the measurement accuracy, such wave effects have to be subtracted in the image analysis. For that purpose, a wavenumber-frequency analysis was applied to a space-time image (STI) generated in the space-time image velocimetry (STIV) analysis and succeeded in eliminating the wavegenerated pattern contained in the texture in STI. It was made clear that turbulence-generated texture propagates at the speed of surface flow.

scholarly journals EVALUATION OF THE IBO RIVER FLOOD SIMULATION BY USING SPACE TIME IMAGE VELOCIMETRY

2014 ◽  
Vol 70 (4) ◽  
pp. I_619-I_624
Author(s):  
Genki KUMANO ◽  
Ichiro FUJITA ◽  
Kayo ASAMI ◽  
Akihiko NAKAYAMA ◽  
Takeshi KAWATANI
Keyword(s):  
Space Time ◽  
Flood Simulation ◽  
Image Velocimetry ◽  
River Flood ◽  
Time Image

scholarly journals An improvement of the Space-Time Image Velocimetry combined with a new denoising method for estimating river discharge

2021 ◽  
Vol 77 ◽  
pp. 101864
Author(s):  
Haoyuan Zhao ◽  
Hua Chen ◽  
Bingyi Liu ◽  
Weigao Liu ◽  
Chong-Yu Xu ◽  
...  
Keyword(s):  
River Discharge ◽  
Space Time ◽  
Denoising Method ◽  
Image Velocimetry ◽  
Time Image

scholarly journals Two-Dimensional Space-Time Image Velocimetry for Surface Flow Field of Mountain Rivers Based on UAV Video

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaolin Han ◽  
Kebing Chen ◽  
Qiang Zhong ◽  
Qigang Chen ◽  
Fujun Wang ◽  
...  
Keyword(s):  
Flow Field ◽  
Field Measurement ◽  
Space Time ◽  
Surface Flow ◽  
Two Dimensional ◽  
Mountain Rivers ◽  
Image Velocimetry ◽  
Search Line ◽  
Time Image ◽  
Flow Field Measurement

Space-time image velocimetry (STIV) is a promising technique for river surface flow field measurement with the development of unmanned aerial vehicles (UAVs). STIV can give the magnitude of the velocity along the search line set manually thus the application of the STIV needs to determine the flow direction in advance. However, it is impossible to judge the velocity direction at any points before measurement in most mountainous rivers due to their complex terrain. A two-dimensional STIV is proposed in this study to obtain the magnitude and direction of the velocity automatically. The direction of river flow is independently determined by rotating the search line to find the space-time image which has the most prominent oblique stripes. The performance of the two-dimensional STIV is examined in the simulated images and the field measurements including the Xiasi River measurements and the Kuye River measurements, which prove it is a reliable method for the surface flow field measurement of mountain rivers.

scholarly journals Improving Accuracy and Robustness of Space-Time Image Velocimetry (STIV) with Deep Learning

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2079
Author(s):  
Ken Watanabe ◽  
Ichiro Fujita ◽  
Makiko Iguchi ◽  
Makoto Hasegawa
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Measurement System ◽  
River Flow ◽  
Time Measurement ◽  
Space Time ◽  
Ease Of Use ◽  
Real Time Measurement ◽  
Image Velocimetry ◽  
Time Image

Image-based river flow measurement methods have been attracting attention because of their ease of use and safety. Among the image-based methods, the space-time image velocimetry (STIV) technique is regarded as a powerful tool for measuring the streamwise flow because of its high measurement accuracy and robustness. However, depending on the image shooting environment such as stormy weather or nighttime, the conventional automatic analysis methods may generate incorrect values, which has been a problem in building a real-time measurement system. In this study, we tried to solve this problem by incorporating the deep learning method, which has been successful in the field of image analysis in recent years, into the STIV method. The case studies for the three datasets indicated that deep learning can improve the efficiency of the STIV method and can continuously improve performance by learning additional data. The proposed method is suitable for building a real-time measurement system because it has no tuning parameters that need to be adjusted according to the shooting conditions and the calculation speed is fast enough for real-time measurement.

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