3D Digital Image Correlation (DIC) using StereoPi
Posted: Tue Jun 23, 2020 10:33 am
I am working on a 3D digital image correlation (DIC) set-up which uses the StereoPi system. DIC is a technique to analyze images containing contrast such as speckles to compute 3D locations (shape) and if tracked over time offers the ability to measure 3D displacement. Gradients of the full field 3D displacement data then offer strain and other derived deformation data which is relevant to the biomechanical research that I do.
While at the MIT Media Lab Dr. Dana Solav and our team built a large 360 degrees 3D DIC system featuring a circular array of Raspberry Pi's and cameras. This was for 3D shape and 3D displacement (and deformation) imaging e.g. to scan lower limbs to build custom prosthetic devices. (accidentally it also enables matrix style "bullet time imaging").
Video:
https://www.youtube.com/watch?v=DC9ifDJ7lvo&t=
Paper:
https://ieeexplore.ieee.org/document/8371235
Software:
https://github.com/MultiDIC/MultiDIC
My plan here is to create a small and low cost version of the above featuring only 2 cameras. This would be useful for imaging deformation during mechanical testing procedures (e.g. tensile uniaxial testing). Besides the simplicity of the StereoPi it also seems to offer the closest thing to true simultaneous imaging, which is really difficult to get right when aiming to trigger a large array of separate Raspberry Pi systems. Therefore I'll also aim to push the imaging speed to image more rapid deformations that were previously available.
What I've done so far:
1) Created a system to mount the cameras separately (not the regular parallel stereo configuration)
2) Added time-lapse photography acquisition button (and associated settings in the configuration file) to the index.php network interface
Next steps:
1) Perform DIC calibration steps, using dotted calibration object and checkerboard pattern
2) Apply a speckled pattern to a test objecct
3) Acquire a time-lapse series of images of the speckled object while it is deforming
4) Optimize resolution/imaging speed to push the maximum speed
5) Deployment of the technique to study soft tissue deformations e.g. the foot (to understand biomechanics of diabetic foot ulcers) and also the breast during clinical mammography procedures.
6) Publish the set-up in a journal like the Journal of Open Hardware (https://openhardware.metajnl.com/)
I'll posts updates here (and on Twitter @KMMoerman https://twitter.com/KMMoerman) as I make progress and will shares codes on GitHub.
While at the MIT Media Lab Dr. Dana Solav and our team built a large 360 degrees 3D DIC system featuring a circular array of Raspberry Pi's and cameras. This was for 3D shape and 3D displacement (and deformation) imaging e.g. to scan lower limbs to build custom prosthetic devices. (accidentally it also enables matrix style "bullet time imaging").
Video:
https://www.youtube.com/watch?v=DC9ifDJ7lvo&t=
Paper:
https://ieeexplore.ieee.org/document/8371235
Software:
https://github.com/MultiDIC/MultiDIC
My plan here is to create a small and low cost version of the above featuring only 2 cameras. This would be useful for imaging deformation during mechanical testing procedures (e.g. tensile uniaxial testing). Besides the simplicity of the StereoPi it also seems to offer the closest thing to true simultaneous imaging, which is really difficult to get right when aiming to trigger a large array of separate Raspberry Pi systems. Therefore I'll also aim to push the imaging speed to image more rapid deformations that were previously available.
What I've done so far:
1) Created a system to mount the cameras separately (not the regular parallel stereo configuration)
2) Added time-lapse photography acquisition button (and associated settings in the configuration file) to the index.php network interface
Next steps:
1) Perform DIC calibration steps, using dotted calibration object and checkerboard pattern
2) Apply a speckled pattern to a test objecct
3) Acquire a time-lapse series of images of the speckled object while it is deforming
4) Optimize resolution/imaging speed to push the maximum speed
5) Deployment of the technique to study soft tissue deformations e.g. the foot (to understand biomechanics of diabetic foot ulcers) and also the breast during clinical mammography procedures.
6) Publish the set-up in a journal like the Journal of Open Hardware (https://openhardware.metajnl.com/)
I'll posts updates here (and on Twitter @KMMoerman https://twitter.com/KMMoerman) as I make progress and will shares codes on GitHub.