# Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images
## 3 Experimental Results
### Matching Results
#### 3.2.2 Comparison with Multi-Modality Image Matching Methods  
In this section, a group of matching results on infrared and visible images is given out to show the performance comparison of DDCE against multi-modality image matching methods. The multi-modality image matching methods used for comparison include PIIFD, MMSURF, PCEHD, LGHD, RIFT, and MSCB. Introductions of these methods are listed in Table 4. This section will present the visual results and performance analyses of DDCE and the matching methods listed in Table 4.  
Figure 5 shows the matching results of PIIFD, MMSURF, PCEHD, LGHD, RIFT, MSCB, and DDCE on a pair of infrared and visible images successively. Similarly, redlines indicate the wrong matches and green lines indicate correct matches. In a similar manner to Section 3.2.1, the number and the percentage of correct matches of each method in Figure 5 are shown in Table 5.  
As shown in Table 5, DDCE achieves the second largest number and the third highest percentage of correct matches. Based on the consistency of edge structures, DDCE can achieve superior performance on the number and the percentage of correct matches. According to Table 5 and Figure 5, two conclusions are given out as follows.  
(1) There is a significant difference of gradient information between infrared and visible images. The matching performance of gradient-based methods including PIIFD, MMSURF, and MSCB is worse than edge-based methods. Even though gradient orientation reversal and gradient magnitude modification are adopted to establish feature descriptions for infrared and visible images, local detail discrepancies can also cause the dissimilarity of feature descriptions. These results indicate that it is difficult to establish similar descriptions through the gradient information.  
(2) Structure information maintains a certain degree of similarity in infrared and visible images. By using image structure information, edge-based methods including PCEHD, LGHD, and DDCE achieve better matching performance than other methods. Although LGHD and PCEHD achieve higher percentage of correct matches, LGHD and PCEHD obtain fewer correct matches than other methods. RIFT obtains the largest number of correct matches. RIFT extracts dense features of images by detecting corners and edge points. However, the percentage of correct matches of RIFT is lower than the other edge-based methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Type** \\ \hline PIIFD & Gradient-Based Method \\ MMSURF & Gradient-Based Method \\ PCEHD & Edge-Based Method \\ LGHD & Edge-Based Method \\ RIFT & Edge-Based Method \\ MSCB & Gradient-Based Method \\ \hline \hline \end{tabular}
\end{table}
Table 4: Multi-modality image matching methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & \
\begin{tabular}{c} **The Number of** \\ **Correct Matches** \\ \end{tabular} &
\begin{tabular}{c} **The Percentage of** \\ **Correct Matches** \\ \end{tabular} \\ \hline PIIFD & 22 & 0.027 \\ MMSURF & 28 & 0.085 \\ PCEHD & 14 & 0.173 \\ LGHD & 23 & 0.284 \\ RIFT & 255 & 0.102 \\ MSCB & 14 & 0.052 \\ DDCE & 99 & 0.128 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The number and percentage of correct matches of multi-modality matching methods.  
Figure 5: _Cont._