Variational optical flow estimation for particle image velocimetry

被引:128
|
作者
Ruhnau, P [1 ]
Kohlberger, T
Schnörr, C
Nobach, H
机构
[1] Univ Mannheim, Dept Math & Comp Sci, Comp Vis Graph & Pattern Recognit Grp, D-68131 Mannheim, Germany
[2] Tech Univ Darmstadt, Chair Fluid Mech & Aerodynam, D-64287 Darmstadt, Germany
关键词
D O I
10.1007/s00348-004-0880-5
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
We introduce a novel class of algorithms for evaluating PIV image pairs. The mathematical basis is a continuous variational formulation for globally estimating the optical flow vector fields over the whole image. This class of approaches has been known in the field of image processing and computer vision for more than two decades but apparently has not been applied to PIV image pairs so far. We pay particular attention to a multi-scale representation of the image data so as to cope with the quite specific signal structure of particle image pairs. The experimental evaluation shows that a prototypical variational approach competes in noisy real-world scenarios with three alternative approaches especially designed for PIV-sequence evaluation. We outline the potential of the variational method for further developments.
引用
收藏
页码:21 / 32
页数:12
相关论文
共 50 条
  • [1] Variational optical flow estimation for particle image velocimetry
    P. Ruhnau
    T. Kohlberger
    C. Schnörr
    H. Nobach
    Experiments in Fluids, 2005, 38 : 21 - 32
  • [2] Subgrid Variational Optimized Optical Flow Estimation Algorithm for Image Velocimetry
    Xu, Haoxuan
    Wang, Jianping
    Zhang, Ya
    Zhang, Guo
    Xiong, Zhaolong
    SENSORS, 2023, 23 (01)
  • [3] Particle image velocimetry with optical flow
    Quenot, GM
    Pakleza, J
    Kowalewski, TA
    EXPERIMENTS IN FLUIDS, 1998, 25 (03) : 177 - 189
  • [4] Particle image velocimetry with optical flow
    G. M. Quénot
    J. Pakleza
    T. A. Kowalewski
    Experiments in Fluids, 1998, 25 : 177 - 189
  • [5] Particle image models for optical flow-based velocity field estimation in image velocimetry
    Glomb, Grzegorz
    Swirniak, Grzegorz
    OPTICS, PHOTONICS, AND DIGITAL TECHNOLOGIES FOR IMAGING APPLICATIONS V, 2018, 10679
  • [6] A variational approach to adaptive correlation for motion estimation in particle image velocimetry
    Becker, Florian
    Wieneke, Bernhard
    Yuan, Jing
    Schnoerr, Christoph
    PATTERN RECOGNITION, 2008, 5096 : 335 - +
  • [7] Generalization of deep recurrent optical flow estimation for particle-image velocimetry data
    Lagemann, Christian
    Lagemann, Kai
    Mukherjee, Sach
    Schroder, Wolfgang
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2022, 33 (09)
  • [8] Challenges of deep unsupervised optical flow estimation for particle-image velocimetry data
    Lagemann, Christian
    Lagemann, Kai
    Mukherjee, Sach
    Schroder, Wolfgang
    EXPERIMENTS IN FLUIDS, 2024, 65 (03)
  • [9] Challenges of deep unsupervised optical flow estimation for particle-image velocimetry data
    Christian Lagemann
    Kai Lagemann
    Sach Mukherjee
    Wolfgang Schröder
    Experiments in Fluids, 2024, 65
  • [10] The Optical Flow Method Research of Particle Image Velocimetry
    Wang Hongwei
    Huang Zhan
    Gong Jian
    Xiong Hongliang
    2014 ASIA-PACIFIC INTERNATIONAL SYMPOSIUM ON AEROSPACE TECHNOLOGY, APISAT2014, 2015, 99 : 918 - 924