## 3 Implementation and Validation of the Proposed Framework through a One-Inch Block Experiment
### Output Object Visualization  
A total of 50 photogrammetry images, 25 captured by each camera of the HS system, was taken from different positions and orientations towards the specimen. The underexposed input images were improved first before the automatic object detection and close-range photogrammetry, in addition to the SHM procedures. The global histogram for input and associated output images for each enhancement method are shown in Figure 3. Because the measurement accuracy was conducted based on the displacement of the six templates shown in Figure 5, the detailed modification of each point after enhancement at their 2D locations is given in Figure 5. This figure displays the change in gray level and the results clearly show that the intensity is evenly stretched for all points. The clipping effect is observed from the CS and HE methods, i.e., the pure white block is clipped at a maximum of 255 intensity. The gray values at this specific region are outside the sensor dynamic range after enhancement so they are set as the maximum (255) and appear as the clipped peaks in the histogram bins. Another observation is that the HRDC method effectively separates the white and black background, such that the low-level intensity of the dark background is visually clear in Figure 5e. Meanwhile, CLAHE softens the clipping  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Camera Type** & **High-Speed (HS)** & **Standalone DSLR (SD)** \\ \hline Standalone & No & Yes \\ Sensor & CMOS & CMOS \\ Color & Monochrome & Monochrome \\ Depth, \(n\) & 8 & 8 \\ Input image size, \(w\ \times\ h\) (pixel) & 2560 \(\times\) 2048 & 5184 \(\times\) 2912 \\ \(f\)-stop number & \(f/14\) & \(f/8\) \\ Shutter speed & 1/3940 & 1/50 \\ File type &.tiff & jpg \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vision-based system configuration sets for one-inch block validation experiment.  
Figure 4: Experimental setup using a standard one-inch (25.4 mm) block inserted into a sliding verification bar.  
effect that is evident in the HE method. It limits template brightness by setting a threshold of 0.01 pixel, thus avoiding oversaturation. The HRIO method also confines the gray level distribution within the sensor dynamic range without the clipping effect.  
The radii of each point in Figure 5 that are identified as an object from NCC template matching are listed in Table 2 for each enhancement algorithm. The search window for the object is set as a 5.0-pixel minimum to allow automatic detection of the center. From Table 2, the pixel length in each direction is not uniform and there is approximately a scale factor of 1.2 due to the difference. Because the images were taken from different angles, the appearance of the object was not always in a circular shape. Therefore, instead of detecting a circle feature, an ellipse threshold of 2.0 pixels was selected to check the similarity of the radius in each direction. When an ellipse feature was detected, the center of the search window defines the ellipse center based on the minimum threshold average length of 2.0 pixels in each direction. The results given in Table 2 show the range of 19-21 pixels and 15-17 pixels for the first and second radius, respectively. The variations within each enhancement method are at the largest using the HRIO method, and at the minimum when CS and HE methods are used. The radii also have variations within each point due to the applied enhancement method, with slightly more percentages for points 2 and 3.  
Figure 5: Gray level distribution of measured templates before (**top-left**) and after enhancement procedures (**a–e**).