# 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 l} \hline \hline
**Det/Desc** & **AKAZE** & **BRIEF** & **BRISK** & **DAISY** & **FREAK** & **KAZE** & **ORB** & **SIFT** & **SURF** \\ \hline
**AGAST** & NC & 0.91869 & 0.92354 & 0.90394 & 0.90831 & NC & 0.92501 & 0.91243 & 0.00000 \\
**AKAZE** & 0.92945 & 0.87339 & 0.92881 & 0.91095 & 0.91303 & 0.91170 & 0.92436 & 0.91196 & 0.89611 \\
**BRISK** & NC & 0.92252 & 0.88374 & 0.91531 & 0.92080 & NC & 0.85495 & 0.91904 & 0.91795 \\
**FAST** & NC & 0.91688 & 0.92586 & 0.91589 & 0.90936 & NC & 0.91269 & 0.90447 & 0.91535 \\
**GFTT** & NC & 0.90719 & 0.92617 & 0.92024 & 0.91344 & NC & 0.89364 & 0.90513 & 0.00000 \\
**KAZE** & NC & 0.91274 & 0.89847 & 0.92245 & 0.90594 & 0.91285 & 0.91383 & 0.89385 & 0.91251 \\
**MSD** & NC & 0.85767 & 0.88436 & 0.90943 & 0.90857 & NC & 0.88385 & NC & 0.91923 \\
**MSER** & NC & 0.00000 & 0.91407 & 0.00000 & 0.00000 & NC & 0.00000 & 0.91407 \\
**ORB** & NC & 0.88139 & 0.86445 & 0.88829 & 0.00000 & NC & 0.90260 & 0.83293 & 0.91399 \\
**SIFT** & NC & 0.90636 & 0.88154 & 0.91642 & 0.86461 & NC & NC & 0.91924 & 0.91398 \\
**Star** & NC & 0.90999 & 0.00000 & 0.89682 & 0.88858 & NC & 0.87969 & 0.88514 & 0.91265 \\
**SURF** & NC & 0.91988 & 0.91946 & 0.90890 & 0.91036 & NC & 0.92380 & 0.91110 & 0.90780 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Execution time for different feature detector–descriptor combinations for dataset 1. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Det/Desc** & **AKAZE** & **BRIEF** & **BRISK** & **DAISY** & **FREAK** & **KAZE** & **ORB** & **SIFT** & **SURF** \\ \hline
**AGAST** & NC & 0.94355 & 0.95077 & 0.93748 & 0.93947 & NC & 0.95000 & 0.94221 & 0.00000 \\
**AKAZE** & 0.95330 & 0.92549 & 0.95267 & 0.94204 & 0.94344 & 0.94460 & 0.95137 & 0.94286 & 0.93521 \\
**BRISK** & NC & 0.94916 & 0.92968 & 0.94548 & 0.94889 & NC & 0.91886 & 0.94745 & 0.94628 \\
**FAST** & NC & 0.94720 & 0.95091 & 0.94546 & 0.94126 & NC & 0.94487 & 0.93825 & 0.94504 \\
**GFTT** & NC & 0.94087 & 0.95315 & 0.94655 & 0.94392 & NC & 0.93165 & 0.93825 & 0.00000 \\
**KAZE** & NC & 0.94545 & 0.93638 & 0.94845 & 0.93915 & 0.94436 & 0.93596 & 0.94456 \\
**MSD** & NC & 0.92137 & 0.92986 & 0.94078 & 0.94171 & NC & 0.93221 & NC & 0.94786 \\
**MSER** & NC & 0.00000 & 0.94379 & 0.00000 & 0.00000 & NC & 0.00000 & 0.0000 & 0.94379 \\
**ORB** & NC & 0.92842 & 0.92445 & 0.93269 & 0.00000 & NC & 0.90059 & 0.91069 & 0.94448 \\
**SIFT** & NC & 0.9383 & 0.92854 & 0.94578 & 0.92059 & NC & NC & 0.94881 & 0.94438 \\
**Star** & NC & 0.94192 & 0.00000 & 0.93588 & 0.92741 & NC & 0.92715 & 0.92577 & 0.94362 \\
**SURF** & NC & 0.94779 & 0.94702 & 0.94071 & 0.94120 & NC & 0.95054 & 0.94099 &obtained using both the combinations is also good because they have sufficient matches. AKAZE + AKAZE also has a good inlier ratio, and the quality of stitched image is also superior to all other combinations. This is because the number of matches is good and are well spread over the image. It also takes less execution time. GFTT + BRISK, AKAZE + ORB, AKAZE + BRISK and FAST + BRISK all have average inlier ratio, but the quality of stitched image is very good. This may be because these combinations produce more false matches, which reduces the inlier ratio, and the correct matches are well spread over the image, which leads to a better quality stitched image. BRISK + ORB and SIFT + FREAK have low inlier ratio and degraded quality of the stitched image. Overall, AKAZE + AKAZE has very good inlier ratio, generating very good stitched images with relatively short execution time (Table 7), which implies that AKAZE + AKAZE is best suited for stitching dataset 1. Figure 5 shows results obtained using AKAZE + AKAZE, BRISK + ORB and SIFT + FREAK combinations for dataset 1. The first column in each row displays the initial matches for that particular detector-descriptor combination, and the second column shows the correct matches for the detector-descriptor pair. The last column shows the stitched image obtained using that particular combination.