A functional error analysis of differential optical flow methods

被引:4
作者
Kumashiro, Keishi [1 ]
Steinberg, Adam M. [2 ]
Yano, Masayuki [1 ]
机构
[1] Univ Toronto, Inst Aerosp Studies, Toronto, ON M3H 5T6, Canada
[2] Georgia Inst Technol, Daniel Guggenheim Sch Aerosp Engn, Atlanta, GA 30332 USA
基金
加拿大创新基金会;
关键词
PARTICLE IMAGE VELOCIMETRY; CONFIDENCE MEASURE; DENSE ESTIMATION; FLUID-FLOW; COMPUTATION; PIV; TURBULENCE; DYNAMICS; MODELS;
D O I
10.1007/s00348-021-03244-1
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
We analyze the sources of error in differential optical flow methods using techniques for the analysis of partial differential equations. We first derive an a priori error bound for the estimated optical flow field. We then systematically interpret this error bound and show that the estimation error is primarily bounded by the best-fit approximation error-which quantifies the fidelity with which one can represent the true optical flow field by a chosen or learned set of basis functions-divided by a stability constant-which quantifies one's ability to infer the optical flow field given the information content of the acquired data. We also show that the estimation error is bounded by effects associated with the finite temporal and spatial resolution of the acquired data. In particular, we show that the main finite resolution effects are related to the finite differencing and time averaging of the measured intensity fields. Finally, we demonstrate the error bound numerically using synthetic three-dimensional data sets based on direct numerical simulations of homogeneous isotropic turbulence and transitional boundary layer flow provided by Johns Hopkins University (Li et al. in J Turbul 9:N31, 2008; Zaki in Flow Turbul Combust in 91(3):451-473, 2013). [GRAPHICS] .
引用
收藏
页数:17
相关论文
共 68 条
[61]   ProbFlow: Joint Optical Flow and Uncertainty Estimation [J].
Wannenwetsch, Anne S. ;
Keuper, Margret ;
Roth, Stefan .
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2017, :1182-1191
[62]   Variational optic flow computation with a spatio-temporal smoothness constraint [J].
Weickert, J ;
Schnörr, C .
JOURNAL OF MATHEMATICAL IMAGING AND VISION, 2001, 14 (03) :245-255
[63]   A theoretical framework for convex regularizers in PDE-based computation of image motion [J].
Weickert, J ;
Schnörr, C .
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2001, 45 (03) :245-264
[64]   Image registration using wavelet-based motion model [J].
Wu, YT ;
Kanade, T ;
Li, CC ;
Cohn, J .
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2000, 38 (02) :129-152
[65]   Hybrid particle image velocimetry with the combination of cross-correlation and optical flow method [J].
Yang, Zifeng ;
Johnson, Mark .
JOURNAL OF VISUALIZATION, 2017, 20 (03) :625-638
[66]   Discrete orthogonal decomposition and variational fluid flow estimation [J].
Yuan, Jing ;
Schnoerr, Christoph ;
Memin, Etienne .
JOURNAL OF MATHEMATICAL IMAGING AND VISION, 2007, 28 (01) :67-80
[67]   From Streaks to Spots and on to Turbulence: Exploring the Dynamics of Boundary Layer Transition [J].
Zaki, Tamer A. .
FLOW TURBULENCE AND COMBUSTION, 2013, 91 (03) :451-473
[68]   An optical flow algorithm based on gradient constancy assumption for PIV image processing [J].
Zhong, Qianglong ;
Yang, Hua ;
Yin, Zhouping .
MEASUREMENT SCIENCE AND TECHNOLOGY, 2017, 28 (05)