# Visible Spectrum and Infra-Red Image Matching: A New Method
## 4 Experimental Setup and Results
### Experimental Results
#### 4.5.2 Comparison of Feature Point Descriptors  
We pair Harris and SIFT detectors with different descriptor types for this comparison. Harris and SIFT detectors are chosen based on their better performance on VS-IR images compared to other detectors as discussed in the previous section. Harris and SIFT detectors are paired one by one  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Detector**} & \multicolumn{2}{c}{**RGB-NIR**} & \multicolumn{2}{c}{**MSD**} \\ \cline{2-5}  & **Correspondences** & **Repeatability** & **Correspondences** & **Repeatbility** \\ \hline SIFT & **903** & **62.3** & 280 & 32.1 \\ SURF & 823 & 54.2 & 198 & 23.2 \\ ORB & 451 & 32.7 & 160 & 26.2 \\ BRISK & 430 & 30.1 & 124 & 14.5 \\ FAST & 675 & 52.5 & 217 & 28.3 \\ Harris & 740 & 56.7 & **324** & **35.2** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average number of correspondences and repeatability scores (%) achieved by different detectors on RGB-NIR and multimodal stereo dataset (MSD) datasets. Maximum value in each column is highlighted with bold face font.  
Figure 4: Image matching frame work for performance evaluation of feature points on VS and near infra-red/long-wave-infra-red (NIR/LWIR) images of the test set.  
with seventeen different descriptor types to evaluate the performances of different detector-descriptor combinations on RGB-NIR and MSD datasets and to identify the best detector-descriptor combinations for VS-IR images. The comparison is based on average matching and precision scores. We use a notation detector+descriptor to represent a detector-descriptor pair. For example SIFT + ORB means that detector is SIFT and the descriptor is ORB. BFDM is used for descriptor matching.  
Table 2 shows average matching score. SIFT + SIFT achieves a matching score of 34% on RGB-NIR dataset whereas Harris+SIFT achieves 24% matching score. SIFT + MN-SIFT achieves the best matching score of 46% on RGB-NIR dataset and outperforms all other descriptor combinations with SIFT detector. In case of Harris detector, Harris + HoDM demonstrates the best performance. It can be seen that descriptors combination with SIFT detector gives better results compared to pairing descriptors with Harris detector. The matching scores achieved on MSD are very low compared to RGB-NIR dataset. This is due to very high textural and intensity changes between VS-LWIR images compared to RGB-NIR images. The comparison shows that SIFT + HoDM obtains better results and outperforms all others detector-descriptor combinations on MSD dataset. Similarly, Harris + HoDM also demonstrates good results on MSD.  
Table 3 shows average precision score obtained by different detector-descriptor combinations. SIFT+MN-SIFT and Harris+HoDM demonstrates the best precision scores on RGB-NIR dataset whereas SIFT+HoDM and Harris+HoDM outperform others on MSD. The above comparisons show that combining SIFT detector with different descriptors produces better results than combining descriptors with Harris detector. Thats why, we use SIFT detector to detect feature points to compute MN-SIFT and Reg-SIFT descriptors, in the proposed method.