On improving the accuracy of self-calibrated stereo digital image correlation system

被引:6
|
作者
Thiruselvam, N. Iniyan [1 ]
Subramanian, S. J. [2 ]
机构
[1] Indian Inst Technol Madras, Dept Engn Design, Chennai, Tamil Nadu, India
[2] PhotoGAUGE India Private Ltd, Chennai, Tamil Nadu, India
关键词
camera self-calibration; photogrammetry; convergent imaging configuration; feature tracking; stereo DIC; DSLR camera; macro lens; CAMERA CALIBRATION; STRAIN-MEASUREMENT;
D O I
10.1088/1361-6501/abae3b
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Stereo digital image correlation (stereo DIC), a full-field deformation measurement technique, is increasingly being used to resolve strains at mu m-length scale by using microscope-like imaging systems. Self calibration of these imaging systems is more cost-effective and convenient than the conventional target-based calibration. Though the use of self-calibrated stereo DIC systems has already been reported, less attention has been paid to improving the accuracy of these systems. In the present work, we improve the accuracy of a self-calibrated stereo DIC system, which is composed of two full-frame DSLR cameras coupled to macro lenses and is used for testing ASTM E8M sub-sized flat dog-bone specimens. First, we collect the images of two of the speckled test specimens that subtend an angle of 12 degrees between them using an f/25 aperture. Our image-collection strategy leads to a convergent imaging configuration with viewpoints that range from -45 degrees to 45 degrees across two perpendicular directions. Next, we process the collected images in a commercial photogrammetric calibration software by using more than nine image points for computing each object point. We validate our findings on a rigid-body motion test and a uniaxial tensile experiment, and we observe an excellent agreement between the stereo-DIC measurements and the ground truth. Using our findings, the reprojection error of self calibration is improved from 0.3 pixel to 0.1 pixel. The error in the stereo-DIC strain measurements is always less than 3.4% with the improvements made to self calibration, whereas it is as large as 7.6% without them.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] A study on the planar rectification of self-calibrated stereo images
    Lee, JS
    Oh, PY
    [J]. CCCT 2003, VOL 3, PROCEEDINGS, 2003, : 465 - 470
  • [2] Random phase-shifting digital holography based on a self-calibrated system
    Xia, Peng
    Wang, Qinghua
    Ri, Shien
    [J]. OPTICS EXPRESS, 2020, 28 (14): : 19988 - 19996
  • [3] Mixed Visual Control Method for Robots With Self-Calibrated Stereo Rig
    Shen, Yang
    Xu, De
    Tan, Min
    Yu, Junzhi
    [J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2010, 59 (02) : 470 - 479
  • [4] Improving the performance of speckle correlation imaging by using a speckle refinement method with self-calibrated homomorphic filtering
    Liu, Yang
    Cui, Guangmang
    Shi, Shigong
    Liao, Fu
    Cui, Weize
    Zhao, Jufeng
    [J]. OPTICS AND LASER TECHNOLOGY, 2024, 179
  • [5] Self-calibrated correlation imaging with k-space variant correlation functions
    Li, Yu
    Edalati, Masoud
    Du, Xingfu
    Wang, Hui
    Cao, Jie J.
    [J]. MAGNETIC RESONANCE IN MEDICINE, 2018, 79 (03) : 1483 - 1494
  • [6] Digitally self-calibrated pipelined Analog-to-Digital Converter
    Bernal, O.
    Bony, F.
    Laquerre, P.
    Lescure, M.
    [J]. 2006 IEEE INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE PROCEEDINGS, VOLS 1-5, 2006, : 900 - +
  • [7] Star-Effect Simulation for Photography Using Self-calibrated Stereo Vision
    Liu, Dongwei
    Geng, Haokun
    Klette, Reinhard
    [J]. IMAGE AND VIDEO TECHNOLOGY, PSIVT 2015, 2016, 9431 : 228 - 240
  • [8] Self-Calibrated Multiple-Lane Detection System
    Jiang, Yan
    Gao, Feng
    Xu, Guoyan
    [J]. 2010 IEEE-ION POSITION LOCATION AND NAVIGATION SYMPOSIUM PLANS, 2010, : 1204 - 1208
  • [9] SCPA-Net: Self-calibrated pyramid aggregation for image dehazing
    Chen, Zhihua
    Zhou, Yu
    Li, Ran
    Li, Ping
    Sheng, Bin
    [J]. COMPUTER ANIMATION AND VIRTUAL WORLDS, 2022, 33 (3-4)
  • [10] Self-calibrated Attention Residual Network for Image Super-Resolution
    Rong, Anqi
    Zhao, Li
    Huang, Pengcheng
    Xu, Jiawei
    [J]. 2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2020, : 3325 - 3332