# 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
**Det/Desc** & **AKAZE** & **BRIEF** & **BRISK** & **DAISY** & **FREAK** & **KAZE** & **ORB** & **SIFT** & **SURF** \\ \hline
**AGAST** & NC & 0.96231 & 0.92468 & 0.96185 & 0.96158 & NC & 0.96206 & 0.96236 & 0.00000 \\
**AKAZE** & 0.96815 & 0.96361 & 0.95989 & 0.96350 & 0.96638 & 0.96505 & 0.96240 & 0.95716 & 0.96387 \\
**BRISK** & NC & 0.96483 & 0.96040 & 0.96593 & 0.96617 & NC & 0.96667 & 0.95559 & 0.96320 \\
**FAST** & NC & 0.95133 & 0.96271 & 0.96375 & 0.96016 & NC & 0.96053 & 0.96549 & 0.96425 \\
**GFTT** & NC & 0.95884 & 0.0000 & 0.96299 & 0.95429 & NC & 0.94643 & 0.96266 & 0.00000 \\
**KAZE** & NC & 0.95427 & 0.96394 & 0.96309 & 0.96688 & 0.95725 & 0.96788 & 0.96099 & 0.96684 \\
**MSD** & NC & 0.94700 & 0.00000 & 0.96353 & 0.96082 & NC & 0.95147 & NC & 0.96173 \\
**MSER** & NC & 0.00000 & 0.00000 & 0.96803 & 0.00000 & NC & 0.00000 & 0.94032 & 0.96544 \\
**ORB** & NC & 0.92883 & 0.92786 & 0.93556 & 0.00000 & NC & 0.92064 & 0.95945 & 0.96191 \\
**SIFT** & NC & 0.95878 & 0.95433 & 0.96179 & 0.96767 & NC & NC & 0.96255 & 0.96283 \\
**Star** & NC & 0.96508 & 0.00000 & 0.94677 & 0.00000 & NC & 0.96008 & 0.95532 & 0.95894 \\
**SURF** & NC & 0.96285 & 0.94828 & 0.96626 & 0.94667 & NC & 0.96015 & 0.96206 & 0.96117 \\ \hline \end{tabular}
\end{table} 
Table 11: FSIM for different feature detector–descriptor combinations for dataset 2.  
image quality is also very good. This is because AKAZE + AKAZE has a good number of matched points that are well spread across the image. KAZE + ORB, SIFT + FREAK, KAZE + FREAK and AKAZE + FREAK have average inlier ratio in comparison to other combinations, but the quality of stitched image is good. The inlier ratio is low because of the higher number of initial matched points and smaller number of correct matched points in comparison to initial matched points, but correct matched points are sufficient to obtain an accurate homography metric, which in turn gives a good quality of stitched image. AGAST + BRISK has low inlier ratio and poor stitched image quality because of a smaller number of matched points. SIFT + BRIEF and AKAZE + SIFT also generate a poor quality of stitched image because of the low inlier ratio. Once again, AKAZE + AKAZE generated a very good quality of stitched image with relatively short execution time (Table 13), which makes AKAZE + AKAZE the best choice for stitching dataset 2, which contains outdoor scenes with irregular features such as trees. Figure 7 shows results obtained using AKAZE + AKAZE, AGAST + BRISK and ORB + BRISK combinations for dataset 2. Each row in the figure presents a detector-descriptor combination, where the first column illustrates the initial matches for that particular pair, the second column shows the correct matches, and the last column exhibits the stitched image produced using that combination. Figure 8 shows the output stitched images of the top six combinations which produce a better quality of stitched image in comparison to all other combinations for dataset 3. It is observed that the AKAZE + AKAZE combination gives the best-quality image. We found one more combination, AGAST + BRIEF which also gives a better quality of stitched image. Figure 8: Image stitching outputs obtained for different feature detector–descriptor combinations for dataset 3. Figure 7: Initial matches, correct matches and stitched images for different feature detector–descriptor combinations for dataset 2. For dataset 3, MSD + BRISK, ORB + BRISK, SURF + BRISK and GFTT + BRISK have very good inlier ratio, but the quality of stitched image is poor because of a smaller number of matched points. This also implies that the BRISK descriptor is not suitable for stitching indoor scene images. AKAZE + SIFT has a good inlier ratio, but the quality of stitched image is poor because matched points are not well spread across the image. AKAZE + AKAZE has a good inlier ratio, and the stitched image quality is also very good because it has sufficient number of matched points that are well spread across the images.