논문
High-gradient pattern image velocimetry (HGPIV)
학술지
Advances in Water Resources
저자
유호준,김동수,Marian
발표일
20220101
We present a new Image Velocimetry (IV) hybrid that estimates vector fields from images with widely different
visualization pattern sizes such as those encountered in riverine bedform migration. The IV approach is obtained
by complementing the Cross-correlation Method (CCM) with algorithms of Optical Flow Methods (OFM). The
OFM procedures are first applied to automatically determine the optimal Search Windows (SWs) over the whole
imaged area. Subsequently, the CCM utilized the established SWs to locally resolve velocity fields associated with
the bedform movement. The new approach, labeled herein as High-Gradient Pattern IV (HGPIV), combines the
advantages of both parent techniques to improve the accuracy and spatial resolution of the resultant global
velocity field and significantly reduces the computational time. The HGPIV validation consists of comparing its
results with those obtained with the CCM approaches applied for estimating the velocity field associated with
bedform migration in a large river.
visualization pattern sizes such as those encountered in riverine bedform migration. The IV approach is obtained
by complementing the Cross-correlation Method (CCM) with algorithms of Optical Flow Methods (OFM). The
OFM procedures are first applied to automatically determine the optimal Search Windows (SWs) over the whole
imaged area. Subsequently, the CCM utilized the established SWs to locally resolve velocity fields associated with
the bedform movement. The new approach, labeled herein as High-Gradient Pattern IV (HGPIV), combines the
advantages of both parent techniques to improve the accuracy and spatial resolution of the resultant global
velocity field and significantly reduces the computational time. The HGPIV validation consists of comparing its
results with those obtained with the CCM approaches applied for estimating the velocity field associated with
bedform migration in a large river.