# Cross-Modal Image Registration via Rasterized Parameter Prediction for Object Tracking
## 5 Results and Analysis  
### Overall Results  
Table 1 shows a comparison of the overall performance between our image registration method and competing methods. We implemented image registration experiments on the MS-COCO visible light image dataset and the FLIR infrared-visible light image dataset with both labelled and unlabelled images. Note that all the annotated labels were used for performance evaluation only, and were not used in model training. It can be observed from these results that our proposed image registration method outperforms competing methods in terms of all the evaluation metrics on both the MS-COCO and FLIR datasets.  
Specifically, we first observe that the VFIS model improves the RMSE of the UDHN model by 1.8% on the MS-COCO visible light image dataset. This might be attributed to the use of cost volume computation in the VFIS, which performs stereoscopic matching of the pixel feature vectors of two images. Our proposed method improves the RMSE of the VFIS model by 10.5%, validating our proposed design for registration parameter estimation.  
The second observation is that the experimental results of the SIFT+RANSAC model on the FLIR infrared-visible light multimodal dataset (RMSE 17.8542) decline sharply compared to the results on the MS-COCO visible light dataset (RMSE 859.4847). However, this is not unexpected. The SIFT algorithm cannot extract consistent features from infrared and visible light images for registration parameter estimation, resulting in a large number of matching failures. On the other hand, the experimental results show that the UDHN model with similarity measure enhancement is successfully applied in infrared-visible light cross-modal image matching (RMSE 24.7125), indicating that the similarity measurement algorithm can improve the spatial offset resolution of the Mean Absolute Error (MAE) metrics in multimodal image registration.  
Finally, our proposed method achieves state-of-the-art results on the FLIR infrared-visible light multimodal dataset (RMSE 15.9170), validating our proposed design for transformation parameter estimation. In addition, our proposed model achieves state-of-the-art results on the unlabelled FLIR infrared-visible light multimodal dataset. This suggests that our proposed model has excellent generalization performance on the infrared-visible light multimodal image registration task.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Model** & **RMSE** & **CC** & **SSIM** & **MI** & **PSNR** \\ \hline \multirow{4}{*}{MS-COCO} & SIFT+RANSAC & 17.8542 & 0.9716 & 0.8787 & 2.2536 & 29.6583 \\  & UDHN & 8.4506 & 0.9126 & 0.8662 & 2.1918 & 21.9387 \\  & VFIS & 8.2986 & 0.9493 & 0.8761 & 2.3268 & 22.2943 \\  & Ours & 7.4214 & 0.9708 & 0.8915 & 2.4001 & 30.0036 \\ \hline \multirow{4}{*}{FLIR (labelled)} & SIFT+RANSAC & 859.4847 & \(\backslash\) & \(\backslash\) & \(\backslash\) \\  & UDHN & 24.7125 & 0.0394 & 0.4060 & 0.6678 & 10.7463 \\  & VFIS & 19.9754 & 0.1885 & 0.4262 & 0.8160 & 10.9331 \\  & Ours & 15.9170 & 0.3505 & 0.4425 & 0.9274 & 11.1414 \\ \hline \multirow{4}{*}{FLIR} & UDHN+CSM & \(\backslash\) & 0.0224 & 0.3997 & 0.6572 & 9.8765 \\  & VFIS & \(\backslash\) & 0.1849 & 0.4215 & 0.8169 & 10.9210 \\ \cline{1-1}  & Ours & \(\backslash\) & 0.3623 & 0.4437 & 0.9042 & 10.9934 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overall results of model comparison for image registration on the MS-COCO and FLIR datasets. MS-COCO is a large-scale visible light image dataset provided by Microsoft that contains more than 200,000 images and covers a variety of real-life scenarios [54]. MS FLIR is a multimodal image dataset provided by the Teledyne FLIR Company; it contains more than 8000 infrared-visible image pairs, which mainly focus on cars and driving scenes [55].