# A Topology Based Automatic Registration Method for Infrared and Polarized Coupled Imaging
## 4 Conclusions  
For the registration of infrared images and polarization parameter images, we completed the two following works: one to obtain the common registration basis for the image to be registered by segmentation; the other to obtain the registration control points by skeleton to improve the robustness to viewing angle difference and image deformation. The experimental results show that the performance of SSR is better than that of two classical registration algorithms and state-of-art multimodal algorithm in infrared and polarization parameter image registration. Objectively, compared with the traditional algorithms that can run the results, the average accuracy and operation speeds are increased by 38.36% and 150.56%, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Indices of Synthetic Image**} & \multicolumn{2}{c}{**Indices of Real World Image**} \\ \cline{2-5}  & **Time** & **IoU** & **Time** & **IoU** \\ \hline SSR & 1.31 s & 96.67\% & 1.76 s & 91.02\% \\ Manual & 9 min+ & 93.49\% & 15 min+ & 94.49\% \\ Mean Squares & 5.60 s & 73.35\% & / & / \\ Mattes MI & 4.20 s & 82.52\% & / & / \\ Phase & 0.85 s & 69.84\% & / & / \\ correlation & 10.36 s & 69.29\% & 6.99 s & 53.94\% \\ PSO & 10.72 s & 68.63\% & 6.47 s & 56.08\% \\ BSO & / & / & 4.95 s & 53.99\% \\ GA & / & / & 9.19 s & 55.80\% \\ MIND & 154.41 s & / & 16.17 s & / \\ \hline \hline \end{tabular}
\end{table}
Table 3: Indices of intensity-based registration experiment.  
Figure 9: The IoU of 30 pairs of real-world images. The hingers indicate the 25 th, 50 th, and 75 th percentiles of accuracy, respectively. The whiskers extend to the min and max data points not considered outliers, and the circles are outliers.  
From the results of image fusion in Figure 6 after registration, we can see that the vein information from the visible polarization parameter image, and the perspective information from the infrared image, are all reflected. The amount of information in subsequent fusion images increased. It also proved the validity and application value of the SSR registration algorithm. The future performance of SSR in optical multi-sensor detection is expected.  
In future steps, we can consider using the classification method of artificial intelligence to segment the same object of the two images accurately, and then carry out the registration based on this.  
Conceptualization, A.Z. and D.H.; methodology, A.Z.; experiment, A.Z. and K.Z.; resources, Q.F. and K.Z.; writing--original draft preparation, A.Z.; writing--review and editing, A.Z., D.H. and Q.F.; project administration, H.J.; funding acquisition, H.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Changchun University of Science and Technology, China. [PERSON], National Natural Science Foundation of China (Major Program), grant number 618930960, 61890963, and Special Fund for Research on National Major Research Instruments, grant number 62127813.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.