## 3 Implementation and Validation of the Proposed Framework through a One-Inch Block Experiment
### Effect of Image Enhancement on the Object Identification in the Close-Range Photogrammetry  
The automatic object identification procedure using the ellipse assumption and NCC matching was described previously in Section 3.2. The example of accurate identification is shown in Figure 6a, in which the object center is detected and positioned at the center with correct registration following the white rings. As a result of the predefined window search and threshold, these rings were sometimes detected as objects, so their center coordinates and residuals were computed in the preliminary object orientation, as shown in Figure 6c. The white rings were detected separately from the circular center and are considered objects. Therefore, these rings cannot be grouped into a single object or correctly registered as a single template. The object center can also be detected but when the matching is not convergent, the detected object is unable to be registered, like the example in Figure 6e. Ellipse shape assumption also may detect a few non-object features, such as the bolts or stair reflections in Figure 6d. These features may cause the photogrammetry image to fail in bundle adjustment computation if they are more dominant in the image plane, as shown in Figure 6f. This image was excluded from automatic bundle adjustment and the photogrammetry cannot be completed when most objects are incorrectly identified in each image plane. The results of object identifications in photogrammetry based on each enhancement algorithm are listed in Table 4. If the 28 templates are all visible in 50 photogrammetry images, the correct object identification should result in 1400 objects. However, these images were taken from different positions, with some pictures taken closer to the specimen. The templates for the top specimen are lost in this position; therefore, the correct identifications in Table 4 comprise fewer than 1400 objects. The total numbers given in Table 4 are the results from the summation of the correct, incorrect, and non-object identification, from which are then subtracted by the unidentified objects. It is observed that haze removal-based algorithms identified 80% of the objects correctly but have slightly higher percentages of non-object identification. Almost 20% of the images enhanced by the HRDC procedure failed as these images consist of incorrect object detection. Furthermore, histogram-based enhancements have lower accuracy of approximately 60% with 0.4% or less unidentified objects. Incorrect object identification also has a higher percentage than in the haze removal-based algorithm. However, they are not concentrated into one image but rather distributed within all 50 images, so there are no failed images from either CS, CLAHE, or HE methods. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **Total** & **Correct** & **\%** & **Incorrect** & **\%** & **Non-Object** & **\%** & **Unidentified** & **\%** & **Failed Images** & **(Out of 50)** \\ \hline CS & 1963 & 1233 & 62.81 & 668 & 34.03 & 68 & 3.46 & \(-\)6 & \(-\)0.30 & 0 \\ CLAHE & 1897 & 1215 & 64.05 & 636 & 33.53 & 49 & 2.58 & \(-\)3 & \(-\)0.16 & 0 \\ HE & 2040 & 1278 & 62.65 & 678 & 33.24 & 85 & 4.17 & \(-\)1 & \(-\)0.05 & 0 \\ HRIO & 1466 & 1174 & 80.08 & 201 & 13.71 & 91 & 6.21 & 0 & 0 & 1 \\ HRDC & 1178 & 965 & 81.92 & 178 & 15.11 & 35 & 2.97 & 0 & 0 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Object identification results from 50 photogrammetry images. Figure 6: Examples of an automatic object and non-object detection, identification, and positioning in the photogrammetry procedure. The enhanced images from each procedure were carefully analyzed, confirming that all failed images were excluded in the bundle adjustment procedure. Therefore, despite the failed images and variations in object identification observed in each enhancement method, the bundle adjustment could still reach convergence.