# Adaptive Multi-View Image Mosaic Method for Conveyor Belt Surface Fault Online Detection
## 3 Materials and Methods
### Feature-Based Adaptive Partition and Block Registration
The curves of \(HM^{\prime}{}_{1}(u)\) and its mean are shown in Figure 8e. The curves of \(VD^{\prime}{}_{1}(v)\) and its mean are shown in Figure 8f.The binarization curves \(B_{HMI}(u)\) and \(B_{VD1}(v)\) are obtained as shown in Figure 8g,h, respectively. Suppose \(T_{C}\) = 128 and \(T_{B}\) = 128, the binarization curves \(B_{HMI}{}^{\prime}(u)\) and \(B_{VD1}{}^{\prime}(v)\) obtained after merging and removing operations are shown in Figure 8i,j, respectively. Finally, the ROI is partitioned, and the result is shown in Figure 8b. It can be seen from 8b that most of the defect information of the conveyor belt is contained in the ROI, which reduces the scope of image registration. We further verify the effect of adaptive partition and block operation for the IOI. The original images are shown in Figure 9a. \(L_{f}\) = 61, \(T_{C}\) = 128, \(T_{B}\) = 128 are used to process the original image and extract the ROI, as shown in Figure 9b. The ROI image is selected and divided into 128 \(\times\) 128 blocks. The selected ROI images are shown in Figure 9(c1,c4,d1,e1). The result of 128 \(\times\) 128 blocks for Figure 9(d1) is shown in Figure 10. The position of the image block is represented by (\(r_{i}\), \(c_{i}\)); \(r_{i}\) and \(c_{i}\) are the numbers of rows and columns of the image block arranged according to the matrix, respectively, satisfying \(r_{i}\leq R_{i}\), \(c_{i}\leq C_{i}\), \(B_{i}=R_{i}\times C_{i}\). The number of blocks shown in Figure 10 is 5 \(\times\) 4. Then, select the image block with a larger standard deviation, as shown in Figure 9c-e, where Figure 9(d2,d3) are the image blocks with the largest standard deviation (Block 1) and the second-largest standard deviation (Block 2), and the positions in Figure 10 are (5,3) and (4,3), respectively. It can be seen that the separated image block contains a high amount of feature information, such as conveyor belt joint or surface fault, which is conducive to the extraction of useful features. The results of the ROI adaptive partition and block method are shown in Table 1.  
Figure 8: \(Cont.\)  
Figure 9: \(Cont.\)  
Figure 9: Adaptive partition and block operation. (**a**) Original image. (**a1**) Conveyor belt joint image (Image 1). (**a2**) Longitudinal tear image (Image 2). (**a3**) Hole breakage image (Image 3). (**b**) Region of interest (ROI) partition results. (**b1**) ROI of Image 1. (**b2**) ROI of Image 2. (**b3**) ROI of Image 3. (**c**) Adaptive partition and block operation for Image 1. (**c1**) Extracted ROI image of Image 1. (**c2**) Block 1 of Figure 9(c1). (**c3**) Block 2 of Figure 9(c1). (**c4**) Extracted ROI image of Image 1. (**c5**) Block 1 of Figure 9(c4). (**c6**) Block 2 of Figure 9(c4). (**d**) Adaptive partition and block operation for Image 2. (**d1**) Extracted ROI image of Image 2. (**d2**) Block 1 of Figure 9(d1). (**d3**) Block 2 of Figure 9(d1). (**e**) Adaptive partition and block operation for Image 3. (**e1**) Extracted ROI image of Image 3. (**e2**) Block 1 of Figure 9(e1). (**e3**) Block 2 of Figure 9(e1). To speed up the calculation, we use the SURF algorithm for feature extraction and description of the selected image block. The SURF algorithm includes the following operations: building the Hessian matrix for feature point extraction, building scale space, feature point location, feature point main direction assignment, feature point descriptor generation, and feature point matching. The random sample consensus (RANSAC) algorithm is used to eliminate the outer points and retain the inner points for image registration. The transformation matrix of the image is constructed through the matching point pairs. According to the transformation matrix obtained by feature registration, the corresponding image is transformed. Feature point matching uses the distance function (Euclidean distance) to retrieve the similarity between high-dimensional vectors. In this paper, the K-D tree  
Figure 10: The result of 128 \(\times\) 128 block for the ROI image extracted in Figure 9(d1). (**a**) (1,1). (**b**) (1,2).