TR2021-012

Fusion-Based Image Correlations Framework For Strain Measurement


    •  Shi, L., Liu, D., Umeda, M., Hana, N., "Fusion-Based Image Correlations Framework For Strain Measurement", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), February 2021.
      BibTeX TR2021-012 PDF
      • @inproceedings{Shi2021feb,
      • author = {Shi, Laixi and Liu, Dehong and Umeda, Masaki and Hana, Norihiko},
      • title = {Fusion-Based Image Correlations Framework For Strain Measurement},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2021,
      • month = feb,
      • url = {https://www.merl.com/publications/TR2021-012}
      • }
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  • Research Area:

    Computer Vision

Abstract:

We address the problem of enabling two-dimensional digital image correlation (DIC) for strain measurement on large three-dimensional objects with curved surfaces. It is challenging to acquire full-field qualified images of the surface required by DIC due to geometric distortion and the narrow visual field of the surface that a single image can cover. To overcome this issue, we propose an end-to-end DIC framework incorporating the image fusion principle to achieve full-field strain measurement over the curved surface. With a sequence of blurry images as inputs, we first recover sharp images using blind deconvolution, then project recovered sharp images to the curved surface using camera poses estimated by our proposed perspective-n-point (PnP) method called RRWLM. Images on the curved surface are stitched and then unfolded for strain analysis using DIC. Numerical experiments are conducted to validate our framework using RRWLM with comparisons to existing methods