# Utilization of Merge-Sorting Method to Improve Stitching Efficiency in Multi-Scene Image Stitching
## 5 Experimental Results and Analysis  
In this section, we describe the experiment and the analysis of multiple scenes. The environment used in our experiments employed OpenCV-3.4.2 on a 2.30 GHz CPU with an Intel(R) Core (TM) i5-6300 HQ processor and Windows 10 operating system. The image data used in the experiments described in this section were obtained manually using the camera of a Samsung Galaxy S10+ mobile phone and a phone holder. We compared the matching time required for the feature points of images ab and cd shown in Figure 1. In order to obtain more suitable data, we repeated the experiment five times using the same images with each method and then took the average of these five experiments as the comparison data, as shown in Table 1. It can be seen from Table 1 that the time spent computing and matching the feature points with the three methods increased with increasing image pixels. For image ab, SURF took the most time among the three algorithms, followed by SIFT, while A-KAE required the least amount of time, about 50% that of SIFT and 20% that of SURF. For image cd, SIFT took the most time, followed by SURF. A-KAE still required the least amount of time, about 38% that of SIFT and 43% that of SURF, demonstrating an improvement in the efficiency of image feature point matching. Figure 4: Stitching of unordered images of two scenes. Scene 1 consists of four images; scene 2 consists of five images. We used two scenes (ordered and unordered images) to verify that the MSIS method worked. Scene 1 comprised 8 (1280 \(\times\) 2862) ordered images Figures 5-8 show the results for the stitching of scene 1 with the three methods., Scene 2 comprised 14 (1280 \(\times\) 1556) unordered images Figures 9-12 show the results of the stitching of scene 2 with the three methods. The method based on the binary tree model demonstrated obvious stitching lines in the result for the orderly image stitching with scene 1, and the overlapping areas on both sides of the stitching lines were not well fused together, resulting in a poor stitching effect. For scene 2, in the final stitching process, the image was obviously compressed towards the middle, and the final boundary was distorted, causing the boundary information to be blurred. For the stitching method with the left-to-right approach, the stitched image for scene 1 had stitching line shadows and the final boundary had serious distortion shadow phenomena, resulting in loss of boundary information. For scene 2, there was a serious black shadow at the final boundary of the stitched image, resulting in loss of boundary information and an incompletely stitched image. The MSIS method combines the merge sorting-based model and the optimal seam line method, making it possible to address the phenomenon of stitching line shadows in the stitched image and weaken the distortion of the final boundary in scene 1, reducing the loss of boundary information. In scene 2, the MSIS method was also able to address the phenomenon of distortion at the last boundary of the image well, and it preserved the boundary information and restored the real scene well. With regard to image stitching efficiency, we repeated the experiment five times using the same image data for each scene and then took the average value for the five experiments as the comparison data, as shown in Table 2. For the ordered image stitching with scene 1, the time required by the method proposed in this paper was reduced by about 30% compared to the binary tree-based model, and the time required was only about 5% more than with the left-to-right method but with a greatly improved stitching effect. For the unordered image stitching with scene 2, the method in this paper not only ensured good results but also took the least amount of time among the three methods. For scene 2, the time required by the MSIS method was reduced by about 10% compared to the binary tree-based model and by about 13% compared to the left-to-right stitching method, demonstrating an improvement in the stitching efficiency with unordered images.