# Integrated Image Processing Toolset for Tracking Direction of Metal Grain Deformation
## 4 Experimental Results
To support these initial  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Processing**} & \multirow{2}{*}{**Thresholding**} & \multicolumn{6}{c}{**Performance Evaluation Metrics**} \\ \cline{3-10}  & & & **Accuracy** & **F1-Score** & **AUC** & **NRM** & **BER** & **PNSR** & **DRD** & **MPM** \\ \hline \multirow{4}{*}{Raw \ \begin{tabular}{c} Speci- \\ men \\ \end{tabular}  } & \multirow{4}{*}{CLAHE} & GD-HBT & 0.977 & 0.985 & 0.986 & 0.014 & 1.432 & 16.381 & 7.298 & 0.002 \\  & & Otsu’s & 0.964 & 0.903 & 0.911 & 0.089 & 8.9 & 14.459 & 12.183 & 0.001 \\  & & Adaptive & 0.964 & 0.901 & 0.909 & 0.091 & 9.067 & 14.38 & 12.524 & 0.001 \\  & & Global & 0.974 & 0.932 & 0.936 & 0.064 & 6.395 & 15.826 & 8.075 & 0.001 \\  & & GD-HBT & 0.972 & 0.983 & 0.983 & 0.017 & 1.717 & 15.591 & 9.185 & 0.003 \\  & & Otsu’s & 0.936 & 0.806 & 0.837 & 0.163 & 16.281 & 11.908 & 25.292 & 0.004 \\  & & Adaptive & 0.93 & 0.785 & 0.823 & 0.177 & 17.735 & 11.542 & 27.945 & 0.005 \\  & & Global & 0.956 & 0.876 & 0.889 & 0.111 & 11.068 & 13.547 & 15.929 & 0.002 \\ \hline \multirow{4}{*}{ \begin{tabular}{c} Weld \\ S specimen \\ men \\ \end{tabular}  } & \multirow{4}{*}{CLAHE} & GD-HBT & 0.94 & 0.953 & 0.955 & 0.045 & 4.488 & 12.221 & 7.009 & 0.007 \\  & & Otsu’s & 0.924 & 0.875 & 0.888 & 0.112 & 11.24 & 11.163 & 9.342 & 0.003 \\  & & Adaptive & 0.889 & 0.825 & 0.85 & 0.15 & 14.981 & 9.956 & 13  
observations, a quantitative evaluation was conducted: a feature correlation heatmap and linear regression analysis. The feature correlation heatmap was plotted based on the maximum correlation value, which was determined as the anchor point. Analyzing the model's performance  
Figure 9: The final results of the 50-scope stitched image and analysis of training time: (**a**) raw and (**b**) weld specimens. using a correlation heatmap allowed us to understand the behavior of the algorithm. Feature correlation indicates the confidence level of the algorithm for each overlapping point. As shown in Figure 10, the image-stitching algorithm based on BLM exhibited satisfying confidence values of 0.953 \(\pm\) 0.013 and 0.913 \(\pm\) 0.061 for the raw and weld datasets, respectively. However, on the weld dataset, there was a significant reduction in the correlation value from iterations 175 to 180. From Figure 10b, it appears that ambiguous results were obtained in the region without grains, which was the mounting region. In this case, the mounting area was extracted without any unique features (blank spots). This condition could not provide a sufficient trigger for the algorithm to determine an anchor point. Thus, the confidence value indicates that the algorithm found an unclear area, which may signal the need for human involvement. To prevent this, observer must ensure that each image dataset contains a grain area. The experiments revealed that the ratio of the grain TtD of the mounting area that can be solved using the BLM algorithm was up to 15%. It improved by up to 10% on the weld dataset with a smaller grain size, which explains why the presence of more unique features on a dataset is expected to give a better probability of detection during the stitching process. Figure 10: Heatmap analysis of confidence value on 50-scope datasets: (**a**) raw and (**b**) weld specimens. To create a credible ground-truth dataset, the validation images were captured at each overlapping point between the four edges of the image (Figure S10). Validation images are used to determine and validate the similarity of the grain shape trends of each ground-truth. This method successfully generated a proper and credible model containing the \(x\) and \(y\) coordinates of the ground-truth model as the input value for linear regression analysis to test the predicted anchor point model. To make a generalized assessment, the 200-scope raw and weld datasets were included in this analysis. Thereafter, the predicted and ground-truth models were plotted using linear regression analysis based on the least-squares methods.