# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 4 Results and Discussion
SIFT + FREAK has an average inlier ratio in comparison to other combinations, and its quality is poor because it has a smaller number of matched points. AGAST + DAISY has average inlier ratio, but the quality of stitched image is very good because of the sufficient number of correct matches. SIFT + BRIEF, AKAZE + FREAK, AGAST + BRIEF and MSD + FREAK generate a good quality of stitched images because the correct matched points are well spread across the image. AGAST + BRISK, ORB + SIFT and KAZE + ORB have poor inlier ratio and poor quality of stitched image. This is because they have very a smaller number of matched points. The inlier ratio of AKAZE + ORB, KAZE + FREAK and FAST + BRISK is low, and the quality of stitched image is not good because of a smaller number of matched points. Overall, AKZE + AKAZE and AGAST + BRIEF generate a very good quality of stitched image, and both are computationally faster (Table 19) for stitching dataset 3 images. Therefore, it can be concluded that AKAZE + AKZE and AGAST + BRIEF are the best choices for stitching indoor scene images. \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 & 0.93672 & 0.87900 & 0.91742 & 0.90365 & NC & 0.91201 & 0.90072 & 0.00000 \\
**AKAZE** & 0.93895 & 0.86206 & 0.00000 & 0.88116 & 0.91780 & 0.88055 & 0.89891 & 0.86782 & 0.89474 \\
**BRISK** & NC & 0.89504 & 0.88327 & 0.84984 & 0.88831 & NC & 0.00000 & 0.86461 & 0.89528 \\
**FAST** & NC & 0.85610 & 0.90165 & 0.85874 & 0.89234 & NC & 0.85849 & 0.88605 & 0.87461 \\
**GFTT** & NC & 0.90417 & 0.87889 & 0.87673 & 0.84947 & NC & 0.84800 & 0.91618 & 0.00000 \\
**KAZE** & NC & 0.90833 & 0.89154 & 0.88127 & 0.90430 & 0.89230 & 0.88185 & 0.89608 & 0.90372 \\
**MSD** & NC & 0.86812 & 0.90999 & 0.92171 & 0.91473 & NC & 0.89695 & NC & 0.88206 \\
**MSER** & NC & 0.00000 & 0.00000 & 0.00000 & 0.00000 & NC & 0.00000 & 0.00000 & 0.00000 \\
**ORB** & NC & 0.82422 & 0.80792 & 0.85765 & 0.00000 & NC & 0.83008 & 0.81993 & 0.88374 \\
**SIFT** & NC & 0.91806 & 0.84770 & 0.90704 & 0.86804 & NC & NC & 0.91775 & 0.91431 \\
**Star** & NC & 0.00000 & 0.00000 & 0.87590 & 0.00000 & NC & 0.00000 & 0.83340 & 0.00000 \\
**SURF** & NC & 0.90624 & 0.88967 & 0.91287 & 0.89395 & NC & 0.00000 & 0.83408 & 0.90552 \\ \hline \end{tabular}
\end{table} 
Table 17: FSIM for different feature detector–descriptor combinations for dataset 3. \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 & 0.95084 & 0.92338 & 0.94169 & 0.93500 & NC & 0.93938 & 0.93235 & 0.00000 \\
**AKAZE** & 0.95365 & 0.91898 & 0.00000 & 0.92411 & 0.94159 & 0.92430 & 0.93203 & 0.92156 & 0.92990 \\
**BRISK** & NC & 0.93118 & 0.92508 & 0.91395 & 0.93028 & NC & 0.00000 & 0.91996 & 0.93030 \\
**FAST** & NC & 0.91604 & 0.93123 & 0.91919 & 0.92941 & NC & 0.92273 & 0.92661 & 0.92434 \\
**GFTT** & NC & 0.93543 & 0.92320 & 0.92314 & 0.92490 & NC & 0.91504 & 0.94085 & 0.00000 \\
**KAZE** & NC & 0.93654 & 0.92848 & 0.92566 & 0.93346 & 0.92890 & 0.92506 & 0.93035 & 0.93508 \\
**MSD** & NC & 0.91946 & 0.93784 & 0.94359 & 0.94208 & NC & 0.93042 & NC & 0.92493 \\
**MSER** & NC & 0.0000 & 0.00000 & 0.00000 & 0.00000 & NC & 0.00000 & 0.00000 & 0.00000 \\
**ORB** & NC & 0.90867 & 0.90987 & 0.91765 & 0.00000 & NC & 0.91657 & 0.90508 & 0.92584 \\
**SIFT** & NC & 0.94167 & 0.91692 & 0.93650 & 0.92028 & NC & NC & 0.94106 & 0.94027 \\
**Star** & NC & 0.00000 & 0.00000 & 0.92239 & 0.00000 & NC & 0.00000 & 0.91164 & 0.00000 \\
**SURF** & NC & 0.93525 & 0.92751 & 0.93854 & 0.92864 & NC & 0.00000 & 0.91272 & 0.93544 \\ \hline \end{tabular}
\end{table} 
Table 18: VSI for different feature detector–descriptor combinations for dataset 3. Figure 9 shows results obtained using AKAZE + AKAZE, AGAST + BRIEF, ORB + BRISK and GFT + BRISK combinations for dataset 3.