# Well-Distributed Feature Extraction for Image Registration Using Histogram Matching
## 4 Results
### Qualitative Analysis  
Figure 7 shows the effect of using histogram matching before template matching on the numbers of control points (CPs). It can be observed that the use of histogram matching increased the number of CPs. The control points shown in the second row are well-distributed as compared to the CPs shown in the first row. Thus, the proposed histogram based method provided more distinctive CPs as compared to the non-histogram based methods. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CPs** & **Data** & **SIFT** & **SURF** & **SSD** & **NCC** & **FZNCC** \\ \hline Initial CPs (Reference image) & & 616,244 & 48,010 & 24,320 & 24,320 & 24,320 \\ Initial CPs (Sensed image) & Set 1 & 383,285 & 28,528 & 24,320 & 24,320 & 24,320 \\ Corresponding CPs & & 6970 & 5038 & 24,320 & 24,320 & 24,320 \\ Refined CPs & & 2558 & 1052 & 3570 & 6449 & 8235 \\ \hline Initial CPs (Reference image) & & 67,902 & 30,897 & 24,320 & 24,320 & 24,320 \\ Initial CPs (Sensed image) & Set 2 & 66,740 & 49,253 & 24,320 & 24,320 & 24,320 \\ Corresponding CPs & & 10,815 & 3984 & 24,320 & 24,320 & 24,320 \\ Refined CPs & & 6465 & 1939 & 6767 & 7883 & 10,269 \\ \hline Initial CPs (Reference image) & & 157,492 & 37,714 & 24,320 & 24,320 & 24,320 \\ Initial CPs (Sensed image) & Set 3 & 148,849 & 43,155 & 24,320 & 24,320 & 24,320 \\ Corresponding CPs & & 9878 & 3304 & 24,320 & 24,320 & 24,320 \\ Refined CPs & & 4913 & 1106 & 8927 & 11,622 & 15,807 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Extracted CPs comparison. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **Data** & **SIFT** & **SURF** & **SSD** & **NCC** & **FZNCC** \\ \hline Reference image & & 0.0042 & 0.0219 & 0.1468 & 0.2652 & 0.3386 \\ Sensed image & & 0.0067 & 0.0369 & 0.1468 & 0.2652 & 0.3386 \\ \hline Reference image & & 0.0952 & 0.0628 & 0.2782 & 0.3241 & 0.4222 \\ Sensed image & & 0.0969 & 0.0394 & 0.2782 & 0.3241 & 0.4222 \\ \hline Reference image & & 0.0312 & 0.0293 & 0.3671 & 0.4779 & 0.6500 \\ Sensed image & & 0.0330 & 0.0256 & 0.3671 & 0.4779 & 0.6500 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CPR comparison. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **SIFT** & **SURF** & **SSD** & **NCC** & **FZNCC** \\ \hline Set 1 & 2.1606 & 3.5117 & 3.5218 & 2.8628 & 1.7490 \\ Set 2 & 1.3509 & 1.8455 & 2.5200 & 1.9755 & 1.0249 \\ Set 3 & 1.2126 & 2.1384 & 1.6349 & 1.3708 & 0.8797 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy comparison in terms of RMSE. The CPs distribution quality [10,25] is an important factor for registration accuracy. Figure 8 highlights the distribution of correct CPs for different feature-based and area-based methods for Dataset 3. For a clear visualization, we used \(1/10\) of sampled CPs. In the figure, extracted and refined CPs using feature-based methods SIFT and SURF are limited and not well-distributed in low contrast area, whereas area based methods SSD, NCC and FZNCC provided more CPs with better distribution. Among the area-based methods, it is clear in Figure 8e that the proposed method provided more distinctive and well-distributed control points. It shows the superiority of the proposed method over the others in terms of number of CPs with their better distribution. Figure 9 shows the resultant images obtained through the feature-based methods (SIFT in Figure 9a and SURF in Figure 9b) and area-based methods (SSD in Figure 9c, NCC in Figure 9d and FZNCC in Figure 9e) for Dataset 3. The figure is composed of pseudo color: red, sensed image; green, reference image; and blue, reference image. If the sensed image is not registered accurately, the red color appears. The white boxes represent the local registration error. In the figure, it can be observed that the registration errors (red colored areas) are visible in the resultant images obtained from the conventional methods SIFT, SURF, SSD and NCC, whereas the proposed method can be clearly distinguished visually, and provided better RGB images compared to the conventional methods.