The subpixel resolution of optical-flow-based modal analysis

被引:115
|
作者
Javh, Jaka [1 ]
Slavic, Janko [1 ]
Boltezar, Miha [1 ]
机构
[1] Univ Ljubljana, Fac Mech Engn, Askerceva 6, Ljubljana 1000, Slovenia
关键词
Gradient-based optical flow; Modal analysis; Full-field non-contact displacement measurement; Subpixel resolution; Operational displacement shapes; Photogrammetry; REGISTRATION ALGORITHMS; IMAGE; PERFORMANCE; EFFICIENCY;
D O I
10.1016/j.ymssp.2016.11.009
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
This research looks at the possibilities for full-field, non-contact, displacement measurements based on high-speed video analyses. A simplified gradient-based optical flow method, optimised for subpixel harmonic displacements, is used to predict the resolution potential. The simplification assumes an image-gradient linearity, producing a linear relation between the light intensity and the displacement in the direction of the intensity gradient. The simplicity of the method enables each pixel or small subset to be viewed as a sensor. The resolution potential and the effect of noise are explored theoretically and tested in a synthetic experiment, which is followed by a real experiment. The identified displacement can be smaller than a thousandth of a pixel and subpixel displacements are recognisable, even with a high image noise. The resolution and the signal-to-noise ratio are influenced by the dynamic range of the camera, the subset size and the sampling length. Real-world experiments were performed to validate and demonstrate the method using a monochrome high-speed camera. One-dimensional mode shapes of a steel beam are recognisable even at the maximum displacement amplitude of 0.0008 pixel (equal to 0.2 gm) and multiple out-of-plane mode shapes are recognisable from the high-speed video of a vibrating cymbal.
引用
收藏
页码:89 / 99
页数:11
相关论文
共 50 条
  • [1] Single-pixel optical-flow-based experimental modal analysis
    Tomac, I.
    Slavic, J.
    Gorjup, D.
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 202
  • [2] Method for Optical-Flow-Based Precision Missile Guidance
    Manchester, Ian R.
    Savkin, Andrey V.
    Faruqi, Farhan A.
    IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2008, 44 (03) : 835 - 851
  • [3] Combined space-variant maps for optical-flow-based navigation
    Baratoff, G
    Toepfer, C
    Neumann, H
    BIOLOGICAL CYBERNETICS, 2000, 83 (03) : 199 - 209
  • [4] Combined space-variant maps for optical-flow-based navigation
    Gregory Baratoff
    Christian Toepfer
    Heiko Neumann
    Biological Cybernetics, 2000, 83 : 199 - 209
  • [5] Evolution of robust high speed optical-flow-based landing for autonomous MAVs
    Scheper, Kirk Y. W.
    de Croon, Guido C. H. E.
    ROBOTICS AND AUTONOMOUS SYSTEMS, 2020, 124
  • [6] Improving monocular visual SLAM in dynamic environments: an optical-flow-based approach
    Cheng, Jiyu
    Sun, Yuxiang
    Meng, Max Q-H
    ADVANCED ROBOTICS, 2019, 33 (12) : 576 - 589
  • [7] Generative-AI- and Optical-Flow-Based Aspect Ratio Enhancement of Videos
    Palczewski, Tomasz
    Rao, Anirudh
    Zhu, Yingnan
    2024 16TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND COMPUTING, ICMLC 2024, 2024, : 355 - 362
  • [8] RESTORATION OF SEA SURFACE TEMPERATURE IMAGES BY LEARNING-BASED AND OPTICAL-FLOW-BASED INPAINTING
    Shibata, Satoki
    Iiyama, Masaaki
    Hashimoto, Atsushi
    Minoh, Michihiko
    2017 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME), 2017, : 193 - 198
  • [9] Optical-flow-based approach for the detection of shoreline changes using remote sensing data
    Bouchahma, Majed
    Barhoumi, Walid
    Yan, Wanglin
    Al Wardi, Hamood
    2017 IEEE/ACS 14TH INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA), 2017, : 184 - 189
  • [10] Evaluation of Optical-Flow-Based Feature Matching for Underwater Vehicle's Displacement Estimation
    Chen, Hsin-Hung
    Tsai, Chao-Wei
    2023 IEEE UNDERWATER TECHNOLOGY, UT, 2023,