# Target Image Mask Correction Based on Skeleton Divergence
## 3 Results and Discussion
### Single Frame Comparison of Experimental Results for the Proposed Algorithm and Other Algorithms  
As shown in Figure 5, the mask correction results for the four comparison algorithms are compared with the experimental results in Figure 4d. Compared with CUR and AIF, GOP and IFMcaS maintain a slightly better contour information of the human body, but they are far behind the algorithm in the correction of binary mask details, for example, the mask correction effect on the arm contour and the clothing grid pattern. None of the algorithms in this paper are clear; CUR and AIF lag far behind the proposed algorithm in the correction of edge contours, and the adhesion in the arm part is increased and not easy to separate, because the correction algorithm of this paper is based on skeleton divergence, using skeleton partitioning. The overall strategy of mask correction is established, and the CUR and AIF algorithms are all for the correction of global points. It is easy for the points in different regions to accumulate errors, which make it difficult to accurately classify; GOP utilizes point-surface correlation characteristics, and IFMcaS utilization characteristics. Although the matching integrity score improves the partial mask correction accuracy, it cannot reduce the error accumulation in the detail correction, resulting in poor mask correction effect.  
At the same time, Table 2 shows the time cost for outputting these experimental results of the five algorithms. It should be noted that the time cost of the algorithm proposed in this article is the time it takes to output the result of Figure 4d, and the time cost of the others is the time it takes to output the experimental results of Figure 5. These five time cost values are all obtained under the same preconditions. The time cost is the time it takes for the computer to run the entire mask correction process, starting with a source image input and ending with the corresponding mask correction image output. It is timed and displayed by the running program inside the computer.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Pr** & **Re** & **Sp** & **FPR** & **FNR** & **PWC** & **F1** \\ \hline Prop & 0.703 & 0.896 & 0.987 & 0.013 & 0.104 & 0.611 & 0.755 \\ GOP [25] & 0.692 & 0.795 & 0.983 & 0.017 & 0.205 & 1.128 & 0.567 \\ CUR [26] & 0.688 & 0.676 & 0.980 & 0.020 & 0.324 & 0.882 & 0.631 \\ IFMcaS [8] & 0.575 & 0.777 & 0.972 & 0.028 & 0.223 & 1.217 & 0.526 \\ AIF [27] & 0.674 & 0.538 & 0.979 & 0.021 & 0.462 & 0.783 & 0.421 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of image processing results for various algorithms.  
As shown in Table 2, the calculation cost of the proposed algorithm is lower than that of similar algorithms, mainly because the algorithm uses point cloud regularization constraints, but the merging strategy of this study adopts the strategy of RGB information as the auxiliary depth information. Moreover, in the early stage of the point cloud regularization constraint, the point cloud is refined and pre-processed, and the iterative simplification is carried out by means of increment and hierarchical structure clustering, so that the point cloud sampling is concentrated in the high curvature region, and the mask correction accuracy is ensured. At the same time, the system efficiency is improved.