# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 4 Results and Discussion
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline
**Det/Desc** & **AKAZE** & **BRIEF** & **BRISK** & **DAISY** & **FREAK** & **KAZE** & **ORB** & **SIFT** & **SURF** \\ \hline
**AGAST** & NC & 23.00484 & 16.84685 & 23.08943 & 22.85816 & NC & 22.96099 & 23.30412 & 0.00000 \\
**AKAZE** & 24.34155 & 23.16761 & 22.64696 & 23.29457 & 23.74266 & 23.48015 & 22.90629 & 22.22603 & 23.13500 \\
**BRISK** & NC & 23.60624 & 22.85367 & 23.91664 & 23.55245 & NC & 23.96318 & 22.07020 & 23.05836 \\
**FAST** & NC & 21.63101 & 23.44776 & 23.23622 & 22.62471 & NC & 22.86712 & 23.41391 & 23.32138 \\
**GFTT** & NC & 22.52211 & 0.00000 & 23.22265 & 22.40035 & NC & 20.70295 & 23.13159 & 0.00000 \\
**KAZE** & NC & 22.18398 & 23.23336 & 23.11630 & 24.05871 & 22.54845 & 24.22516 & 22.68412 & 23.78771 \\
**MSD** & NC & 20.99705 & 0.00000 & 23.14319 & 22.60502 & NC & 21.63379 & NC & 22.86339 \\
**MSER** & NC & 0.00000 & 0.00000 & 23.93411 & 0.00000 & NC & 0.00000 & 19.13762 & 23.62422 \\
**ORB** & NC & 17.32199 & 17.22133 & 19.27183 & 0.00000 & NC & 16.34763 & 22.92395 & 22.93993 \\
**SIFT** & NC & 22.68530 & 22.42940 & 23.14216 & 24.28646 & NC & NC & 23.03123 & 23.06673 \\
**Star** & NC & 23.84176 & 0.00000 & 21.00664 & 0.00000 & NC & 22.67085 & 22.06882 & 22.41675 \\
**SURF** & NC & 23.16130 & 21.17148 & 23.72378 & 21.09816 & NC & 22.56551 & 22.94019 & 22.78473 \\ \hline \end{tabular}
\end{table} 
TableFor dataset 2, AKAZE + BRISK and ORB + BRISK both have very good inlier ratio, but the quality of the stitched image is poor; this is because they have a smaller number of correct matched points, which affected the homography metric that in turn resulted into degraded quality of the stitched image. SURF + BRISK and ORB + SIFT also have a very good inlier ratio, but the quality of stitched image is poor; this is because the matched points are not well spread in the image. AKAZE + AKAZE has a very good inlier ratio, and stitched \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 & 0.94422 & 0.84767 & 0.94742 & 0.94294 & NC & 0.94438 & 0.94524 & 0.00000 \\
**AKAZE** & 0.95572 & 0.94936 & 0.94312 & 0.94749 & 0.95098 & 0.94875 & 0.94704 & 0.93782 & 0.94805 \\
**BRISK** & NC & 0.94962 & 0.94632 & 0.95064 & 0.95176 & NC & 0.95355 & 0.93561 & 0.94870 \\
**FAST** & NC & 0.92552 & 0.94729 & 0.94979 & 0.94521 & NC & 0.94667 & 0.95097 & 0.94813 \\
**GFTT** & NC & 0.94101 & 0.00000 & 0.94909 & 0.93461 & NC & 0.92189 & 0.94804 & 0.00000 \\
**KAZE** & NC & 0.93358 & 0.94621 & 0.94696 & 0.95459 & 0.93875 & 0.95381 & 0.94469 & 0.95096 \\
**MSD** & NC & 0.90969 & 0.00000 & 0.94615 & 0.94367 & NC & 0.92711 & NC & 0.94581 \\
**MSER** & NC & 0.00000 & 0.00000 & 0.95358 & 0.00000 & NC & 0.00000 & 0.89024 & 0.95079 \\
**ORB** & NC & 0.86680 & 0.86223 & 0.89071 & 0.00000 & NC & 0.84705 & 0.94044 & 0.94634 \\
**SIFT** & NC & 0.93854 & 0.93378 & 0.94606 & 0.95510 & NC & NC & 0.94793 & 0.94534 \\
**Star** & NC & 0.95047 & 0.00000 & 0.91623 & 0.00000 & NC & 0.94523 & 0.93481 & 0.94156 \\
**SURF** & NC & 0.94505 & 0.91856 & 0.95114 & 0.91616 & NC & 0.94321 & 0.9494 & 0.94530 \\ \hline \end{tabular}
\end{table} 
Table 13: Execution time for different feature detector–descriptor combinations for dataset 2.