# Integrated Image Processing Toolset for Tracking Direction of Metal Grain Deformation
## 4 Experimental Results
To reinforce the image-intensity stacking analysis, five open-source FD methods, known as the local interest points, were utilized to support the analysis: Binary Robust Invariant Scalable Keypoints (BRISK) [42], the Harris-Stephens algorithm (HARIS) [62], Maximally Stable Extremal Regions (MSER) [63], Oriented FAST and Rotated BRIEF (ORB) [64], and Speeded Up Robust Features (SURF) [65]. The idea was to extract the image features to compare the image-matching performance (Figures S11-S15). The linear regression model analysis was utilized to analyze the shifting value of the feature coordinates to better explain the global performance of image-matching algorithms. From the 760 images on each dataset assessed in this analysis, up to 3.5 \(\times\) 10\({}^{5}\) feature points were successfully extracted for the input parameters. Figure 11: Linear regression model analysis based on least-squares methods for assessing the model performance and behavior. The results found that the matching performance exhibited consistency and a homogeneous performance on each FD in terms of the robustness, visual quality, and a metric parameter (Table 2). The algorithm successfully maintained the RMSE for less than three pixels and MAE for less than two pixels, with an exceptional coefficient of determination that was close to one in both the raw and weld specimens. There was no indication of a performance drop because the average residual remained close to zero. The results are also supported by the SE, \(t\)-statistic, and \(p\)-value values recorded during the testing, which were identical for each FD. This is essential, owing to the fact that the deformation of the metal grain is on the microscopic scale. A fundamental error in determining the global location will cause extensive issues in the in situ assessment of the grain deformation. This encouraging result supports the image-intensity stacking analysis corresponding to the structure locations. Figure 12: Linear regression model analysis based on least-squares methods for assessing the model performance and behavior. Finally, the performance of the boundary tracing algorithm is assessed. As shown in Figure 13, the proposed method successfully traced the grain boundary from a binary image. This algorithm can effectively distinguish between the grain, grain boundary, and background. The reconstruction ability of the tracing algorithm is related to the performance of the binarization process since the algorithm works based on the binary logic step to determine whether there is an absolute discontinuity in the binary value. Furthermore, the boundary coordinates of the specific grains were extracted from the results and integrated with the coordinate values obtained from image stitching and matching. From these steps, we recorded the location of the grain on the global coordinates (Figure 13c). This approach can be widely utilized for the observation of metal grains, such as the analysis of the geometry, grain area, and deformation. Figure 13: Grain boundary extraction processes: (**a**) target grain labelling using built-it MATLAB apps “Image Labeler”, (**b**) extraction results using Moore–Neighbor tracing algorithm with updated [PERSON]’s stopping criteria, (**c**) plot of specific grain on global location coordinate by integrating the stitched image, matching, and boundary coordinate.