## 2 Methods and Materials
### Data Processing
#### 2.4.3 Rapid Video Imaging and Digital Image Correlation  
Optical imaging produces a sequence of images, which was used for the DIC analysis. The majority of modifications in the Ncorr codes were made to automate and accelerate the process without affecting the DIC algorithm itself. Each recording was processed individually. The processing was divided into two parts. We first analysed the entire sequence of images with respect to the \"cyclic\" reference image taken close to the mean load immediately after the test was paused. In a second part of the analysis, we compared two reference images: the \"cyclic\" reference image corresponding to the finally deformed state and the one of the undamaged sample. The second step allowed us to find the total strains with respect to the undeformed specimen. However, when plastic deformations are large, the surface becomes so severely deformed that the correlation between the undeformed and finally deformed specimens is not reliable any more or it can disappear completely. Nonetheless, the analysis of the image sequence with respect to the cyclic load can still deliver the rate of the deformation tensor as a function of the number of cycles.  
Region of Interest and Mask  
To ensure a high quality of strain data in the vicinity of the crack tip, the crack representing discontinuity in strain data must be covered by a virtual mask. If the mask is too wide, valuable data in close proximity to the crack path might become lost, whereas if it does not cover the crack sufficiently, incorrect strain values can be obtained. The precise determination of the crack tip position is, therefore, of crucial importance. There have been many methods proposed in the literature to find the crack tip position (see [15; 57; 113; 114] and references therein). The approach we utilised in the present work is based on the simple observation that the crack was dark, and it was well visible throughout the test. A convolution algorithm was developed to find this dark region, and the end of it was defined as the crack tip. Additionally, the images corresponding to the maximum and minimum load were used to find the motion range within a given recording to make sure the mask was created sufficiently within the borders of the field of view (FOV). An example of the DIC mask created by this algorithm is shown in Figure 8.  
Figure 8: Mask created based on the specimen motion and crack. Lens-induced distortions can be noticed as a curvature of straight judicial lines scratched on the surface.  
Once the images are chosen for the analysis and the mask is applied, the seed points are selected. The algorithm for that was based on Speeded Up Robust Features (SURF) [115], as the features identified in this way are good candidates for distinct points. A similar approach was used by [PERSON] et al. [116]. SURF points were found in the reference image, and they were classified according to their strength. The number of points corresponding to the number of processor threads was selected. To make sure that they were roughly equally spaced, the chosen points were placed as far away from ROI borders, and from each other as possible. Hence, the region for the analysis was approximately evenly distributed between the CPU threads for efficient computation. Once all of these steps were automatised, the DIC analysis could run automatically.