# A Topology Based Automatic Registration Method for Infrared and Polarized Coupled Imaging
## 3 Results and Discussion
### Synthetic Images Experiment  
Two synthetic images were designed to simulate the characteristics of infrared and polarized images. One image contains rich texture details and a consistent gray value, which simulates the polarization parameter image. The other image has a large difference in gray level but few texture features, which simulates the infrared image. In the two images, the target was shifted, zoomed, and slightly rotated to simulate the different imaging positions of the two cameras. In addition, the aspect ratio of the two images was inconsistent to simulate the size differences of the different sensor chips. The registration accuracy rate of SSR reached 96.67% as calculated by IoU (Intersection of Union). From the subjective point of view, the target in the two images completely overlaps as shown in Figure 3.  
We compared several feature-based algorithms such as SURF, MSER, Brisk, FAST, Harris, and MinEigen, and their checkpoint detection, and matched the pair results as shown in Table 1.  
By analyzing the synthetic image pair, none of the feature-based registration algorithms can achieve this registration. Therefore, only their intermediate process data discussions are listed in Table 1. Due to the huge difference in features between images, the experimental results of feature-based algorithms are not good. In the results of the original image pair, SURF, MSER and MinEigen found a large number of feature points, but none of them were matched. FAST and Brisk found fewer mismatched feature points. Harris could not even find a feature point pair. These algorithms fail for various reasons. SURF and MSER are blob feature sensitive. SURF search found features by Hessian matrix, which means the result depends on the gradient of region, while \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multicolumn{2}{c}{**Original Image Pair**} & \multicolumn{2}{c}{**Segmented Image Pair**} \\ \cline{2-5}  & **Detected Points** & **Matched Pairs** & **Detected Points** & **Matched Pairs** \\ \hline SURF & 152-692 & 0 & 88-273 & 2 \\ MSER & 1-1107 & 0 & 0-0 & 0 \\ Brisk & 13-1 & 13 & 64-21 & 0 \\ FAST & 1-1 & 1 & 7-8 & 4 \\ Harris & 8-80 & 0 & 8-79 & 0 \\ MinEigen & 8-87540 & 0 & 8-91 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Feature-based synthetic images registration experiment. Figure 3: Registration pair by SSR: (**a**) is the simulated infrared image, (**b**) is the simulated polarized image, and (**c**) is the registration result. MSER search found extremely bright or dark region; since multimodal images are nonlinear in pixel value, their gradient difference and brightness difference lead to misalignments in SURF and MSER. Brisk, FAST, MinEigen, and Harris are corner sensitive. As Table 1 shows, [PERSON] and [PERSON] found corners of the geometric region in Figure 3a, but the corners in Figure 3b were submerged in texture, and no checkpoint can be paired. FAST found one pair of checkpoints, but it is insufficient for registration. MinEigen was confused by the texture and erroneously derived many points, which obstructed it from finding the best match pairs. Briefly, this algorithm starts by segmenting the synthetic images to achieve a more consistent binary image and then carries out the registration comparison experiment with feature-based algorithms. The analysis of the segmented image pairs yielded similar results. [PERSON], [PERSON], and MiniEigen found sufficient feature points, but could not match them. MSER could not work out feature point pairs. SURF and FAST matched a few pairs, but the results were wrong. After active contour segmentation, the angular features of the target are smooth and the original straight edges arise with slight ripples, which makes the feature registration of the segmented image poor. The experiment shows that this kind of algorithm is extremely unsuitable in our case. Table 1 shows that feature-based registration algorithms basically find out lots of feature points from the synthetic images, but hardly match them.