# Visible Spectrum and Infra-Red Image Matching: A New Method
## 4 Experimental Setup and Results
### Experimental Results
#### 4.5.5 Computational Complexity  
This section presents computational complexity comparisons. All the experiments are performed on a single desktop PC, with Window 7 as operating system and i5-2520M CPU and 16 GB memory space. GPU and parallel computations are not used.  
Table 7 shows the average time taken (in seconds) by different feature point detectors to detect 1000 feature points per image. Ten different images of the same size, that is, 1024 \(\times\) 768 are used and then average time is presented. The comparison shows that the FAST detector takes less time on average to detect feature points compared to other detectors.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Descriptor** & **BFDM** & **TAR**[37] & **FSC**[38] & **LLT**[39] & **PSOSC**[40] & **GLPM**[41] \\ \hline RGB-NIR & SIFT & 52 & 50 & 57 & 60 & 65 & 72 \\ SURF & 25 & 26 & 32 & 31 & 40 & 42 \\ ORB & 59 & 63 & 62 & 69 & 74 & 78 \\ BRISK & 51 & 43 & 54 & 59 & 61 & 68 \\ GOM-SIFT & 57 & 60 & 63 & 69 & 72 & 79 \\ OR-SIFT & 57 & 55 & 59 & 63 & 67 & 72 \\ EOH & 42 & 35 & 47 & 49 & 53 & 58 \\ LBPG & 50 & 58 & 66 & 73 & 76 & 78 \\ LS-SIFT & 61 & 65 & 68 & 70 & 77 & 80 \\ DE-SIFT & 63 & 65 & 69 & 73 & 79 & 81 \\ NG-SIFT & 61 & 63 & 69 & 76 & 82 & 83 \\ MN-SIFT & 70 & 74 & 78 & 83 & 83 & 85 \\ LSS & 48 & 40 & 49 & 53 & 59 & 65 \\ VAR-EOH & 43 & 48 & 49 & 52 & 57 & 69 \\ LSSF & 53 & 58 & 64 & 68 & 72 & 78 \\ MFD & 65 & 69 & 74 & 75 & 80 & 83 \\ HoDM & 68 & 73 & 79 & 83 & 84 & 86 \\ Proposed & **81** & **83** & **87** & **89** & **89** & **94** \\ \hline MSD & SIFT & 3 & 3 & 6 & 8 & 9 & 9 \\ SURF & 1 & 1 & 1 & 4 & 5 & 7 \\ ORB & 2 & 2 & 2 & 6 & 7 & 10 \\ BRISK & 1 & 1 & 2 & 4 & 8 & 10 \\ GOM-SIFT & 3 & 5 & 6 & 6 & 10 & 12 \\ OR-SIFT & 3 & 4 & 7 & 7 & 10 & 11 \\ EOH & 2 & 2 & 3 & 5 & 7 & 10 \\ LBPG & 1 & 5 & 9 & 10 & 14 & 16 \\ LS-SIFT & 1 & 2 & 6 & 13 & 17 & 22 \\ DE-SIFT & 1 & 6 & 13 & 15 & 19 & 25 \\ NG-SIFT & 6 & 15 & 25 & 32 & 41 & 49 \\ MN-SIFT & 9 & 21 & 28 & 35 & 46 & 57 \\ LSS & 3 & 6 & 8 & 7 & 12 & 16 \\ VAR-EOH & 2 & 2 & 4 & 4 & 6 & 8 \\ LSSF & 10 & 14 & 14 & 19 & 20 & 23 \\ MFD & 19 & 25 & 32 & 38 & 45 & 58 \\ HoDM & 23 & 31 & 39 & 50 & 55 & 61 \\ Proposed & **37** & **43** & **48** & **63** & **69** & **73** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average precision scores (%) achieved by different descriptors using different feature matching strategies. SIFT detector is used for feature point detection.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Time (s)** \\ \hline SIFT & 0.566 \\ SURF & 0.179 \\ ORB & 0.075 \\ Brisk & 0.418 \\ Harris & 0.053 \\ FAST & **0.046** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average time taken by different detectors to detect 1000 feature points per image.  
Table 8 shows average time taken (in seconds) by different descriptor construction methods to compute 1000 descriptors per image. Ten different images of the same size, that is, 1024 \(\times\) 679 are used for this comparison. Then, average time is reported in the table. The comparison shows that ORB takes less time on average to compute descriptors compared to others.