The background for this application is the use of stereo digital image correlation (DIC) in thermoforming applications.
Thermoforming is a plastic sheet conversion technology that is often characterised by large out-of-plane displacements. A problem that arises with these large out-of-plane displacements is that, since DIC systems typically work with fixed focal length lenses, the pictures can become unsharp and correlation problems may arise. People performing DIC measurements and expecting large out-of-plane displacements either trust on their experience or use the equations from photography to estimate which lens to choose, where to position the camera and how to set the aperture since these parameters affect the depth of field of the camera. This approach is of course not bad, but the definition of sharpness based on the circle of confusion, which is a human defined parameter, does not reflect the performance of the (digital image) correlation system. Besides, it is a well-known fact that pictures that are not perfectly sharp tend to yield better correlation results since the interpolation between pixels, which is necessary to get sub-pixel information, is better when there is a smooth transition in image gradients (soft edges) than with sudden transitions (sharp edges).
To get the correct DOF value that is useful for DIC, a standardized testing method has been designed based on real camera and lens combinations and based on actual image correlation results. It consists of taking a number of pictures of a speckled target moving backwards. This test is performed twice. First when focussing the camera at each increment and then without refocussing. Both sets of images are then correlated (in 2D) and the results are exported to csv files. The resulting virtual strain (mean and standard deviation) as function of the distance to the target is compared between the sharp and the unsharp pictures. When repeating the test for different starting distances and aperture settings, the complete depth of field of a specific camera/lens combination can be characterised as function of the aperture setting and distance from camera to target.
The current Matlab GUI is meant to automate the procedure of loading the csv files of the sharp and unsharp correlated pictures, to match them and to display the results of the matching: --> depth of field, field of view, resolution as function of the camera/lens, aperture and starting distance.
Bart Van Mieghem (2022). Depth of field calculator based on 2D digital image correlation data (https://www.mathworks.com/matlabcentral/fileexchange/52050-depth-of-field-calculator-based-on-2d-digital-image-correlation-data), MATLAB Central File Exchange. Recuperado .
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