# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 4 Results and Discussion
In the figure, each row represents a unique detector-descriptor combination. The first column demonstrates the initial matches for that particular pair, while the second column showcases the correct matches. The last column displays the resulting stitched image obtained using that specific combination. We also considered six publicly available datasets for image stitching. The input images of the dataset used and the generated stitched output images are given in Appendix A. We examined a pair of deep learning algorithms for image stitching that were recently introduced in the literature. Appendix B contains the results obtained using these deep learning algorithms. Table 20 (dataset 1 column) shows the top ten methods (detector and descriptor combination) for dataset 1. AKAZE + AKAZE is top ranked for dataset 1, and after that BRISK + FREAK and AKAZE + ORB are at second position. Table 20 (dataset 2 column) presents the top ten methods for dataset 2. AKAZE + AKAZE performs the best in dataset 2 and hence ranks first. SIFT + FREAK and Star + BRIEF are at the second position. Table 20 (dataset 3 column) shows the top ten methods for dataset 3. AKAZE + AKAZE is at top position for dataset 3, and AGAST + BRIEF secures the second position. SIFT + BRIEF is in the third position. Finally, the ranking may be adjusted by weighting particular metrics while averaging the results. In future, different weights can be assigned to each metric depending upon the requirements; for example, in real time applications, we can compromise with quality for shorter execution time; therefore, we can assign higher weight to execution time in  
Figure 9: Initial matches, correct matches and stitched images for different feature detector–descriptor combinations for dataset 3. \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 & 1.461 & 1.658 & 1.651 & 1.566 & NC & 1.478 & 1.548 & 1.477 \\
**AKAZE** & 1.442 & 1.576 & 1.722 & 1.792 & 1.53 & 1.81245 & 1.543 & 1.634 & 1.586 \\
**BRISK** & NC & 1.61 & 1.738 & 1.793 & 1.694 & NC & 1.768 & 1.782 & 1.684 \\
**FAST** & NC & 1.593 & 1.529 & 1.67 & 1.426 & NC & 1.562 & 1.722 & 1.477 \\
**GFTT** & NC & 1.431 & 1.836 & 1.613 & 1.374 & NC & 1.53 & 1.44 & 1.416 \\
**KAZE** & NC & 1.671 & 2.023 & 2.073 & 1.687 & 2.0259 & 1.666 & 1.686 & 1.656 \\
**MSD** & NC & 1.946 & 2.023 & 2.102 & 1.855 & NC & 1.865 & NC & 1.925 \\
**MSER** & NC & 100 & 1.702 & 100 & 1.494 & NC & 100 & 100 & 100 \\
**ORB** & NC & 2.229 & 2.171 & 1.757 & 100 & NC & 1.62 & 2.304 & 1.662 \\
**SIFT** & NC & 1.518 & 1.752 & 1.658 & 1.603 & NC & NC & 1.646 & 1.563 \\
**Star** & NC & 1.368 & 100 & 1.629 & 1.406 & NC & 100 & 2.309 & 1.395 \\
**SURF** & NC & 1.522 & 1.614 & 1.639 & 1.532 & NC & 1.472 & 2.785 & 1.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Execution time for different feature detector–descriptor combinations for dataset 3.  
comparison to quality metrics. In virtual tour applications, quality is important, so we can assign quality metrics higher weight than execution time.