# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 4 Results and Discussion
Figure 6 shows the output stitched images of the top six combinations, which produces a better quality of stitched image in comparison to all other combinations for dataset 2. Again, it is observed that the AKAZE + AKAZE combination gives the best-quality image. Figure 5: Initial matches, correct matches and stitched images for different feature detector–descriptor combinations for dataset 1. Figure 6: Image stitching outputs obtained for different feature detector–descriptor combinations for dataset 2. Table 8 shows the inlier ratio obtained using different combinations of feature detector and descriptors for dataset 2. Tables 9-12 presents the values of PSNR, SSIM, FSIM and VSI obtained using various combinations of detector-descriptor pairs for dataset 2. It is observed that the AKAZE + AKAZE combination has highest values of quality metrics for dataset 2. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline
**Det/Desc** & **AKAZE** & **BRIEF** & **BRISK** & **DAISY** & **FREAK** & **KAZE** & **ORB** & **SIFT** & **SURF** \\ \hline
**AGAST** & NC & 0.90377 & 0.85146 & 0.90017 & 0.90272 & NC & 0.90198 & 0.90157 & 0.00000 \\
**AKAZE** & 0.90828 & 0.90134 & 0.89826 & 0.90195 & 0.90504 & 0.90420 & 0.89978 & 0.89541 & 0.90148 \\
**BRISK** & NC & 0.90339 & 0.89830 & 0.90446 & 0.90380 & NC & 0.90609 & 0.89466 & 0.90039 \\
**FAST** & NC & 0.89339 & 0.90131 & 0.90109 & 0.89753 & NC & 0.89865 & 0.90272 & 0.90247 \\
**GFTT** & NC & 0.89805 & 0.00000 & 0.90073 & 0.89472 & NC & 0.88489 & 0.89852 & 0.00000 \\
**KAZE** & NC & 0.89447 & 0.90355 & 0.90123 & 0.90555 & 0.89700 & 0.90768 & 0.89868 & 0.90562 \\
**MSD** & NC & 0.88723 & 0.00000 & 0.90344 & 0.89946 & NC & 0.89348 & NC & 0.90024 \\
**MSER** & NC & 0.00000 & 0.00000 & 0.90696 & 0.00000 & NC & 0.00000 & 0.87682 & 0.90396 \\
**ORB** & NC & 0.85524 & 0.85148 & 0.86754 & 0.00000 & NC & 0.83955 & 0.89755 & 0.90041 \\
**SIFT** & NC & 0.89933 & 0.89299 & 0.90052 & 0.90759 & NC & NC & 0.89993 & 0.90245 \\
**Star** & NC & 0.90369 & 0.00000 & 0.88529 & 0.00000 & NC & 0.89795 & 0.89454 & 0.89701 \\
**SURF** & NC & 0.90221 & 0.88801 & 0.90492 & 0.88590 & NC & 0.89784 & 0.90061 & 0.89932 \\ \hline \end{tabular}
\end{table} 
Table 10: SSIM for different feature detector–descriptor combinations for dataset 2. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline
**Det/Desc** & **AKAZE** & **BRIEF** & **BRISK** & **DAISY** & **FREAK** & **KAZE** & **ORB** & **SIFT** & **SURF** \\ \hline
**AGAST** & NC & 0.64966 & 0.63636 & 0.71163 & 0.65574 & NC & 0.66492 & 0.63415 & 0.00169 \\
**AKAZE** & 0.90000 & 0.73134 & 1.00000 & 0.81776 & 0.76471 & 0.91228 & 0.86792 & 0.59441 & 0.79104 \\
**BRISK** & NC & 0.81452 & 0.92308 & 0.54906 & 0.88889 & NC & 1.00000 & 0.70703 & 0.73200 \\
**FAST** & NC & 0.54984 & 0.83333 & 0.78214 & 0.59091 & NC & 0.62911 & 0.84132 & 0.58296 \\
**GFTT** & NC & 0.78378 & 0.00000 & 0.80180 & 0.50000 & NC & 0.77273 & 0.85714 & 0.08333 \\
**KAZE** & NC & 0.57627 & 0.88235 & 0.86957 & 0.78947 & 0.53266 & 0.87234 & 0.85714 & 0.72549 \\
**MSD** & NC & 0.62500 & 0.66667 & 0.61069 & 1.00000 & NC & 0.73684 & NC & 0.74074 \\
**MSER** & NC & 0.00000 & 0.00000 & 0.89474 & 0.71429 & NC & 1.00000 & 0.88235 & 0.95000 \\
**ORB** & NC & 0.54167 & 1.00000 & 0.55102 & 0.00000 & NC & 0.55556 & 0.90000 & 0.79452 \\
**SIFT** & NC & 0.60345 & 0.85714 & 0.75806 & 0.86667 & NC & NC & 0.88732 & 0.41096 \\
**Star** & NC & 1.00000 & 0.00000 & 0.55263 & 0.50000 & NC & 1.00000 & 0.93478 & 0.87500 \\
**SURF** & NC & 0.59740 & 0.95238 & 0.59006 & 0.63636 & NC & 0.76471 & 0.82514 & 0.77273 \\ \hline \end{tabular}
\end{table} 
Table 8: Inlier ratio for different feature detector–descriptor combinations for dataset 2.