# Target Image Mask Correction Based on Skeleton Divergence
## 3 Results and Discussion
### Multi-Target Mask Correction Effect  
As shown in the multi-objective mask correction described in Figure 4, comparing Figure 3a with Figure 4a, the number of targets in Figure 4a is increased. Similar to the results in Figure 3, with the improvement of the system structure, the algorithm gradually shows better correction of the binary mask, and it is not affected by the increase of the target. Obviously, the increase of the target means that the difficulty of skeleton extraction increases. The effect of the mixture of discrete pixel binary masks on each target on the correction accuracy of the mask will also be enhanced. However, the skeleton extraction in this paper is based on the edge contour information to extract the skeleton. When the  
Figure 3: Single-objective experimental rendering of the proposed algorithm. (**a**) Binary mask effect of the source image, (**b**) mask correction effect using only skeleton-based edge guidance, (**c**) the result of adding TOPSIS and point cloud regularization constraints to the (b) system, and (**d**) binary masking effect of the algorithm. edge contour information is extracted, the two targets will be taken as two. The single connected regions extract the skeleton separately, avoiding the mutual influence between the targets. At the same time, using the edge similarity guidance of the skeleton divergence, the adjacent sub-regions are merged according to the edge similarity, and the skeleton-centered divergence and the adjacent sub-regions are merged. Merging makes the edge features of the pixel regions to be merged more concentrated, which greatly reduces the influence of other target pixel binary masks that are mixed in. Multiple Indicator Comparison of Sequence Experimental Results for the Proposed Algorithm and Other Algorithms  
The proposed algorithm is compared with four other algorithms: GOP [25], CUR [26], IFMcaS [8], and AIF [27]. These are all relatively advanced algorithms in the field of mask optimization. GOP utilizes a point-to-point iterative nearest-point algorithm framework; CUR uses the circumferential shape feature of the local surface to obtain accurate matching points from the initial matching points; IFMcaS is a fusion score splicing and feature matching method to solve the problem of image stitching quality; AIF utilizes the spatial angle invariant feature to stitch the image. The adopted evaluation criteria include precision (Pr), recall (Re), specificity (Sp), false positive rate (FPR), false negative rate (FNR), percentage of wrong classification (PWC), and F-measure (F1), which are defined as follows [28]. \[ \begin{array}{l}\Pr=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}},\mathrm{Re}= \frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}},\mathrm{Sp}=\frac{\mathrm{TN}}{ \mathrm{TN}+\mathrm{FP}},\\ \mathrm{FPR}=\frac{\mathrm{FP}}{\mathrm{TP}+\mathrm{TN}},\mathrm{FNR}=\frac{ \mathrm{FN}}{\mathrm{TP}+\mathrm{FN}},\\ \mathrm{PWC}=\frac{100\mathrm{X}(\mathrm{FN}+\mathrm{FP})}{\mathrm{TP}+\mathrm{ TN}+\mathrm{FN}+\mathrm{FP}},\mathrm{F1}=\frac{2\mathrm{P}\mathrm{P}\mathrm{Re}}{ \mathrm{TP}+\mathrm{Re}}\end{array} . \tag{21}\]  
For these criteria, the true positive (TP) is the number of pixels correctly detected as foreground, the false positive (FP) is the number of pixels erroneously detected as foreground, the true negative (TN) is the number of pixels correctly detected as the background, and the false negative (FN) is the number of pixels erroneously detected as background. As shown in Equation (21), the larger the values of \(\mathrm{Pr}\) (indicating that a method is not sensitive to noise), Re, Sp, and F1 (indicating that a lower amount of correct information is missing in the  
Figure 4: Experimental effect of the proposed algorithm in the multi-objective case. (**a**) Binary mask effect on source image, (**b**) mask correction effect using only skeleton-based edge guidance, (**c**) the result of adding TOPSIS and point cloud regularization constraints to the (**b**) system, and (**d**) binary value of the algorithm mask effect.