## 4 Experimental Results and Analysis
### Comparison with Other Algorithms  
To prove the advantage and significance of the algorithm proposed in this paper, this section compares its the experimental results with a state-of-the-art algorithm in the same dataset (three sets of datasets in Reference [36]). The comparison results are shown in Figures 12-20. Explanation of the meaning of the indicators in the Tables 3-5: MN: number of matching points; FT: Feature extraction time (s); FT: match time; ST: total time. The first set is of images of different affine distortions. The detection results of the reference image feature points, the feature points of the image to be registered, and the image matching results from the adaptive SIFT algorithm and the algorithm in this paper are shown in Figures 12-14. The specific data are shown in Table 3. It can be seen from the results that the algorithm in this paper had fewer matching points. The feature-point extraction and matching times as well as total time were all reduced. The second set is of images of different scales. The detection results of reference image feature points and image feature points to be registered and the adaptive SIFT algorithm and the image matching results obtained by the algorithm in this paper are shown in Figures 15-17. See Table 4 for specific data. It can be seen from the results that compared with the adaptive SIFT algorithm, the algorithm in this paper had fewer matching points, and the feature-point extraction and matching times were reduced as was total time. Figure 12: The extraction result of feature points of the reference image. (**a**) The extraction result of the adaptive RKEM algorithm; (**b**) the extraction result of the algorithm in this paper. Figure 13: The extraction result of feature points of the image to be registered. (**a**) The adaptive RKEM algorithm; (**b**) The extraction result of the algorithm in this paper. Figure 14: Matching results of images with different affine distortions. (**a**) The adaptive RKEM algorithm; (**b**) The algorithm in this paper. Figure 15: Extraction result of feature points of the reference image. (**a**) The adaptive RKEM algorithm; (**b**) The algorithm in this paper. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **MN** & **REMS** & **FT(s)** & **PT(s)** & **ST(s)** \\ \hline SIFT & 479 & 11.429 & 24.24 & 11.504 & 32.744 \\ Adaptive RKEM & 245 & 9.139 & 35.612 & 0.734 & 36.346 \\ This paper & 166 & 8.208 & 15.73 & 5.196 & 20.926 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of matching results of different affine distorted images. The third set is of images of different lighting. The detection results of reference image feature points and image feature points to be registered, and the image matching results obtained by the adaptive SIFT algorithm and the algorithm in this paper are shown in Figures 18-20. See Table 5 for specific data. It can be seen from the results that the algorithm in this paper had fewer matching points, and the feature point extraction and matching times as well as total time were all reduced. Figure 16: The extraction result of feature points of the image to be registered. (**a**) The adaptive RKEM algorithm; (**b**) The algorithm in this paper. Figure 17: Matching results of images with different affine distortion. (**a**) The adaptive RKEM algorithm; (**b**) The algorithm in this paper. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **MN** & **REMS** & **FT(s)** & **PT(s)** & **ST(s)** \\ \hline SIFT & 263 & 15.791 & 3.794 & 0.141 & 3.9235 \\ Adaptive RKEM & 179 & 9.139 & 6.172 & 0.020 & 6.192 \\ This paper & 114 & 8.486 & 2.443 & 0.019 & 2.462 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of matching results of images of different scales images. Figure 19: The extraction result of feature points of the image to be registered. (**a**) The adaptive RKEM algorithm; (**b**) The algorithm in this paper.