## 2 Methodology
### Method Overview
#### 2.4.2 Effective Subregions' Extraction  
After obtaining the comprehensive gray average image \(\overline{G}[I]\) of the image database, it is necessary to extract the effective area in the images \(I_{j}\). Image segmentation is a popular method that can extract feature regions of an image. The adaptive threshold segmentation method is used to extract effective subregions regions [32]. For different subregions of the image, it can calculate different thresholds adaptively. Figure 7 showed the histogram of the gray-level averaged image [37]. We used the gray-level distribution of the histogram to determine the threshold value.  
After applying the adaptive threshold algorithm, we have found the best threshold value (185) as shown in Figure 8. According to the selected threshold value, the coordinates of the highest and lowest points of the effective area were located. As shown in Figure 6, parallel lines were used to divide the effective area into subregions.  
Figure 6: Comprehensive gray average image with subregions.  
Figure 7: Gray histogram of Comprehensive image after gray weighted average.  
then be extracted as described in the next session. In summary, the step-by-step procedures for the effective area extraction are described as follows.  
1. Apply gray-level averaging to all the images in the database, derive the comprehensive image \(\overline{G}[\textbf{I}]\).
2. Generate the grayscale distribution of \(\overline{G}[\textbf{I}]\), search the gray level distribution curve from 0 to find the local maximum \(p_{k}\) (or peak) of the distribution curve. The local minimum between the first highest maximum \(p_{1}\) and the second highest maximum \(p_{2}\) (or peak) is selected as the threshold gray level (\(\delta\)).
3. Apply the adaptive threshold segmentation algorithm to \(\overline{G}[\textbf{I}]\) to obtain an output image. Scan the output image from the top to bottom to search the \(y\) level (\(y_{min}\)) for which the pixel's gray level start to exceed the threshold. Scan the output image from bottom to top to search the \(y\) level (\(y_{max}\)) for which the gray level exceeds the threshold.
4. The image area \(\textbf{S}\) of gray level higher \(\delta\) is then equally subdivided into subregions \(S_{i}\) (e.g., \(N_{r}=5\)). Effective subregions are then extracted from the results of step 3.  
In step 4, the image area \(\textbf{S}\) is divided into \(N_{r}\) subregions. Selection of \(N_{r}\) should ensure the subregion to match with (or greater than) the ANN's input requirement so that neural network function could be fully utilized and the estimation accuracy could be achieved by effective fusion process. A small value of \(N_{r}\) (\(N_{r}\leq 3\)) will reduce the level of fusions and the estimation accuracy, and the proposed method will be similar to a non-fusion method single image approach. Too high value of \(N_{r}\) will cause the subregion images' size smaller than the input requirement (\(224\times 224\)) for the ANN's feature extraction and the pre-trained ANN network function may not be fully utilized. With an image width of 1920 as described in the Section 3, \(N_{r}\) can be chosen between 5 to 8. In this paper, \(N_{r}\) is selected as 5 to ensure that the subregion images' size is greater than the ANNs' input requirements, the ANNs' function could be fully utilized and the accurate estimation results could be achieved.