# Concrete Crack Identification and Image Mosaic Based on Image Processing
## 2 Materials and Methods
### Crack Mosaicing Algorithm Based on SIFT Feature Point Correlation  
#### 2.3.1 Method of Mosaicing  
In the process of acquiring images, a crack may be scattered in multiple images due to various conditions, which increases the difficulty of obtaining crack information. Therefore, it is necessary to splice multiple crack images into a complete crack image to obtain more comprehensive information about the crack. In the process of image stitching, it is necessary to adjust the size and angle of the original second image and then stitch it with the original first image. The precondition for stitching two adjacent images is that they must share an overlapping area. The larger the proportion of the overlapping area is, the higher the stitching precision. The main task of splicing is to determine the overlap degree of adjacent images in the directions of the height and width and then remove  
Figure 8: Central skeleton method. Figure 9: Double edge skeleton method. the overlapping area to obtain a complete panoramic image [39]. The splicing process is shown in Figure 10.  
Image pre-processing primarily corrects the distortion of the original image and suppresses the noise points to prepare for the next step of image registration. Distortion correction can eliminate obvious geometric distortion in the original image, and noise point suppression can avoid the occurrence of some mismatches to the greatest extent. Image registration [40] is mainly used to extract the matching information in the original image and to perform the spatial alignment and greyscale fusion of multiple images taken in the same scene at different moments with different light intensities, different sensors and different angles. Among the constituent steps, feature matching is the basis of image registration. Feature matching refers to the finding of similar parts of two images to be registered based on feature points or greyscale to find the image similar to the search image. A commonly used feature matching method is the SIFT [41; 42; 43; 44; 45] method. The essence of the SIFT algorithm is to find feature points in different scale spaces and calculate the direction of the feature points. These feature points are some very prominent points that do not change due to factors such as the lighting, image size, and noise. However, in the process of matching key points, the SIFT algorithm often produces mismatching, and the mismatching points need to be eliminated later. Therefore, based on the SIFT algorithm, this paper incorporated the feature point correlation comparison and priority matching algorithm. The first step is coarse adjustment, which plays a role in positioning. First, traverse all the feature points on the left and then find the corresponding points of the feature points in the right image. Each feature point may have many corresponding points in the right image. Since the feature point association comparison process is complicated, three feature points are taken as an example. Take any of the three feature points in the left figure and record them as \(P_{1,l},P_{2,l},P_{3  
The second step is to fine tune the alignment. After the first step of feature point positioning, the image mosaic has basically been completed. However, due to the difference in the number of matching points, although the feature points have been correlated, some stitching errors will inevitably occur. An insufficient or excessive number of matching points may also adversely affect stitching accuracy. The second step is performed to avoid these conditions, and the process is as follows. Adjust the coefficient distRatio that controls the number of matching points in the Match.m file to obtain different numbers of matching points. As the number of matching points changes, the stitching result is adjusted accordingly. By adjusting this factor, the stitched image can be fine-tuned. Taking Figure 12 as an example, Figure 12a,b, which are to be stitched, are undistorted images.