# Adaptive Multi-View Image Mosaic Method for Conveyor Belt Surface Fault Online Detection
## 4 Experiments and Results  
The experiment is carried out on the platform, as shown in Figure 12, in order to verify the effectiveness of the method proposed in this paper. Two 2048 \(\times\) 2048 ROIs with the same surface damage content were selected, as shown in Figure 13(a1,b1). Simulation by using MATLAB software programming in Intel i5 CPU computer showed that the method was effective. The ROI images are shown in Figure 13(a2,b2). The block images are shown in Figure 13(a3,a4,b3,b4). For the original image, the ROI image, and the block image, the SURF algorithm with the Hessian matrix determinant response value of 1000 was used for feature extraction and description. Draw the circle on the image with the feature point as the center. The radius of the circle indicates the size of the feature point, and the straight line indicates the direction of the feature point. The feature points are described as shown in Figure 13(a5-a8,b5-b8). The comparison of image feature extraction is shown in Table 2. It can be seen that the number of feature points extracted in the ROI of Figure 13(a1,b1) were 65.52% and 69.71% of the whole image, respectively. The number of feature points extracted from the ROI accounts for a high proportion of the number of feature points in the whole image, reflecting that the ROI is the feature point concentration region of the whole image. However, it took 40.48% and 37.18% of the total image time, respectively, which was less time-consuming. Although the number of feature points extracted from two image blocks was less than the ROI, the feature points extracted had larger feature values and significant features, which met easy registration requirements. Moreover, the two image blocks took less time. The total time was only 16 ms, which was 2.77% and 2.93% of the whole image time, respectively. It is thus suitable for fast registration. For the two images of Figure 13(a1,b1), the different threshold values of Hessian matrix determinant response value, such as 500, 1000, 1500, and 2000, were used to extract the feature points of each image. The comparison in Table 3 shows that with the increase of the threshold value, the number of feature points extracted and the time consumed was reduced. However, the threshold value is important for the block image, because of the small image size. If the threshold value is too large, too few feature points are extracted in the block, which is not conducive to feature point registration. For example, when the threshold value was 2000, the number of feature points shown in Figure 13(b7) was only one. The threshold value of the Hessian matrix determinant response value can be selected as 1000 by comparison. Figure 12: Experimental platform. For the feature points extracted from the original image, ROI image, and block image, we used the RANSAC algorithm to complete the matching of feature points. The matching results are shown in Figure 14. The comparison of registration is shown in Table 4. It can be seen that a sufficient number of registration feature point pairs can be obtained from block images. The time of registration processing on an Intel i5 CPU computer was 5 ms, which was 41.67% of the total image time. Figure 13: Speeded up robust features (SURF) extraction and description. (**a**) Image 1. (**a1**) Original image of Image 1. (**a2**) ROI of Image 1. (**a3**) Block 1 of Image 1. (**a4**) Block 2 of Image 1. (**a5**) Feature points of Figure 13(a1). (**a6**) Feature points of Figure 13(a2). (**a7**) Feature points of Figure 13(a3). (**a8**) Feature points of Figure 13(a4). (**b**) Image 2. (**b1**) Original image of Image 2. (**b2**) ROI of Image 2. (**b3**) Block 1 of Image 2. (**b4**) Block 2 of Image 2. (**b5**) Feature points of Figure 13(b1). (**b6**) Feature points of Figure 13(b2). (**b7**) Feature points of Figure 13(b3). (**b8**) Feature points of Figure 13(b4).