# Research on a Fast Image-Matching Algorithm Based on Nonlinear Filtering
## 3 Experimental Results and Analysis
### Robustness Evaluation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methodologies** & **Bark** & **Bikes** & **Leuven** & **Boat** & **Graf** & **Average** \\ \hline SIFT & 78.90 & 86.03 & 86.26 & 76.00 & 83.80 & 82.20 \\ SURF & 70.72 & 85.72 & 85.34 & 67.30 & 67.03 & 75.22 \\ BRISK & 78.91 & 86.05 & 91.92 & 76.63 & 81.57 & 83.02 \\ ORB & 46.67 & 94.64 & 86.68 & 60.52 & 53.31 & 68.36 \\ Our & **91.83** & **96.21** & **93.28** & **81.22** & **92.90** & **91.09** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average matching correctness of test image sets. Figure 4: Effectiveness on Graf image set. Figure 5a depicts the Bark image dataset from the [PERSON] image library, designed to assess the robustness of algorithms to tree bark texture under various rotation conditions. This dataset features diverse textures covering the bark characteristics of trees, aiming to simulate real-world challenges for evaluating algorithm performance in practical scenarios. From Figure 5a, it can be observed that the improved algorithm proposed in this paper performs significantly better under image rotation conditions compared to the BRISK, ORB, SIFT, and SURF algorithms. In comparison to the SIFT algorithm, the improved algorithm in this paper achieves an average correctness rate that is 12.28% higher. Figure 5b,c depict scenes of lighting changes and image blurring in the Bikes and Leuven datasets, respectively. From Figure 5b, it is evident in the Bikes dataset that the fast image-matching algorithm based on nonlinear filtering consistently outperforms the other algorithms in terms of the correctness rate for each match. In Figure 5c, within the Leuven dataset, the correctness rates for the first three sets of image matches are higher than those of the other four algorithms, and although there are fluctuations when matching with images 1-5, the correctness rate is comparable to that of the other four algorithms. Overall, the fast image-matching algorithm based on nonlinear filtering performs better than other algorithms in scenarios involving lighting changes and image blurring. Figure 5d displays the Boat grayscale rotation dataset. The experimental results indicate that each algorithm processes the images in grayscale and performs feature matching under different rotation angles and focal lengths. From the correctness rates of the first three sets of image matches, it can be observed that all five algorithms exhibit a decreasing trend. However, the improved algorithm proposed in this paper consistently maintains a higher correctness rate compared to the other four algorithms. When matching features with images 1-5, there is a noticeable decrease in correctness rate for the fast image-matching algorithm based on nonlinear filtering, yet it still outperforms the ORB algorithm, maintaining a 5.23% higher average correctness rate compared to the SIFT algorithm. Figure 5e presents the correctness rate of matches in the Graf image dataset from the [PERSON] image library. This dataset is primarily used to test and evaluate the robustness of image feature point-matching algorithms in the face of varying viewpoints and angle changes. It aims to accurately measure and compare the stability and adaptability  
Figure 5: Robustness test curves of different algorithms. of different image feature-matching algorithms from multiple perspectives and dimensions to maintain high-precision matches. The experimental results indicate that when facing different viewpoints and angle changes, the overall correctness rate on each image dataset is higher than that of the ORB, SURF, and BRISK algorithms. Furthermore, the correctness rate is more than 9.1% higher than that of the SIFT algorithm. The implementation of a fast image-matching algorithm based on nonlinear diffusion filtering for scale-space construction, coupled with maximum downsampling, has shown significant performance enhancements across the following five key datasets: Bark, Bikes, Leuven, Boat, and Graf.