| Paper ID | CI-2.2 |
| Paper Title |
FUSION-BASED DIGITAL IMAGE CORRELATION FRAMEWORK FOR STRAIN MEASUREMENT |
| Authors |
Laixi Shi, Carnegie Mellon University, United States; Dehong Liu, Mitsubishi Electric Research Laboratories (MERL), United States; Masaki Umeda, Norihiko Hana, Mitsubishi Electric, Japan |
| Session | CI-2: Computational Imaging for Inverse Problems |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Computational Imaging: [CIF] Computational Image Formation |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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 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. |
