# Visible Spectrum and Infra-Red Image Matching: A New Method
## 4 Experimental Setup and Results
### Experimental Results
#### 4.5.3 Comparison between Proposed Descriptor and State of the Art  
In this section the performance of the proposed descriptor (Reg-SIFT) is compared with different state of the art descriptors. The comparison is based on average matching and precision scores. Only SIFT detector is used as it obtains better results than Harris detector on VS-IR images. Reg-SIFT and other descriptors evaluated in this section are computed for SIFT feature points. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Descriptor**} & \multicolumn{3}{c}{**RGB-NIR**} & \multicolumn{2}{c}{**MSD**} \\ \cline{2-5}  & **SIFT Detector** & **Harris Detector** & **SIFT Detector** & **Harris Detector** \\ \hline SIFT & 34 & 24 & 3 & 1 \\ SURF & 19 & 14 & 1 & 0 \\ ORB & 36 & 28 & 1 & 1 \\ BRISK & 32 & 25 & 1 & 0 \\ GOM-SIFT & 35 & 26 & 4 & 2 \\ OR-SIFT & 35 & 27 & 4 & 3 \\ EOH & 25 & 19 & 3 & 3 \\ LBPG & 32 & 28 & 2 & 1 \\ LS-SIFT & 37 & 29 & 2 & 1 \\ DE-SIFT & 39 & 31 & 2 & 1 \\ NG-SIFT & 38 & 31 & 5 & 4 \\ MN-SIFT & **46** & 36 & 6 & 6 \\ LSS & 31 & 27 & 3 & 2 \\ VAR-EOH & 24 & 17 & 2 & 1 \\ LSSF & 32 & 24 & 7 & 5 \\ MED & 43 & 34 & 11 & 8 \\ HoDM & 45 & **38** & **16** & **14** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average matching score (%) achieved by different detector-descriptor pairs on RGB-NIR and MSD datasets for image matching. Maximum value in each column is highlighted with bold face font. Table 4 shows a comparison between different regression models for computing Reg-SIFT descriptors. The models which are compared are LR, DTR, RFR, SVMR and MLPR. The Reg-SIFT computed with LR model is referred to as \(LR\)-SIFT, similarly for other models. The comparison shows that MLPR model gives better precision and matching scores compared to others. In the subsequent sections we use MLPR for Reg-SIFT, to compare its performance with seventeen different descriptors. Figure 5 shows image matching results obtained on a MSD scene. Each sub-figure shows a reference (left: VS) and a target (right: LWIR) image. SIFT feature points are detected and SIFT, MFD, HoDM, and Reg-SIFT (MLPR) descriptors are computed. Descriptor matching is carried out with BFDM. Correct and false descriptor matches are shown as green and blue lines, respectively. Correct and false descriptor matches are identified with projection error less than or equal to 2 pixels as explained above. It can be seen that SIFT+SIFT gives only one correct match. It obtains matching and precision scores of 0.5% and 0.8%, respectively. SIFT+MFD obtains 9% matching and 24% precision scores. SIFT + HoDM obtains matching and precision scores of 12% and 44%, respectively. Whereas the proposed Reg-SIFT works really well. It obtains matching and precision scores of 20.5% and 72.4%, respectively, (see Figure 5d) and outperforms SIFT, MSD and HoDM. Reg-SIFT obtains almost 8.5% (matching) and 28.4% (precision) better scores than HoDM. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**RGB-NIR**} & \multicolumn{2}{c}{**MSD**} \\ \cline{2-5}  & **Matching** & **Precision** & **Matching** & **Precision** \\ \hline \(LR\)-SIFT & 49 & 71 & 19 & 28 \\ \(DTR\)-SIFT & 52 & 74 & 22 & 31 \\ \(RFR\)-SIFT & 56 & 78 & 25 & 36 \\ \(SVMR\)-SIFT & 54 & 76 & 24 & 34 \\ \(MLP\)-SIFT & **60** & **81** & **27** & **37** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average matching and precision scores (%) achieved by Reg-SIFT using different regression techniques.