# Target Image Mask Correction Based on Skeleton Divergence
## 1 Introduction  
Moving-target mask correction is a process by which a complete target object binary mask is recovered using scattered target pixels. Target pixel mask correction based on foreground motion is a key step in both point-cloud motion stitching and graphics stitching. It is also widely used in 3D target reconstruction, image segmentation, and video frame-rate improvement. Researchers have proposed various algorithms for image binary mask correction, most of which focuses on a single strategy [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. In general, these algorithms fall into two categories: image feature-based methods and model-based optimization methods. Image feature-based methods primarily use the feature information contained in the image to drive mask correction [1]. The splicing of multiple images is achieved using constant local features, which overcomes the influence of the input image's own signal-to-noise ratio and external illumination on the results. However, in the case of mask repair for continuous video frames showing the motion of participants in a scene, there is a lack of precision. [PERSON] et al. [2] presented in detail the image-stitching cost function set in the gradient domain to evaluate the image-stitching quality. Compared with the traditional method, their approach overcomes the influence of luminosity inconsistency, but does notovercome the influence of the external environment. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) proposed a local splicing method for processing parallax. However, their proposed method lacks robustness in environments with small parallax. [PERSON] et al. [PERSON] et al. (2018) proposed a method that visually measures the quality of stitching using the similarity between the stitched image, the input image, and the visibility of the seam. However, their proposed method is still not robust in scenes with environmental change. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) proposed the use of dynamic elements to stitch and compose images. However, their approach lacks precision in the case of multi-target scenes. [PERSON] et al. [PERSON] et al. (2018) proposed an image-stitching method based on a projected uniform plane. However, their proposed method is computationally intensive, and is thus not suitable for practical scene applications. [PERSON] et al. [PERSON] et al. (2018) presented in detail a color-mixing method to eliminate color matching inconsistencies in pre-aligned images. However, the method presented does not overcome the influence of external illumination on experimental results. Consequently, it is computationally expensive, which also affects its practical application. Model-based optimization methods primarily model the mask correction problem and add constraint items to achieve mask correction by minimizing the difference between the mask correction and the real scene. [PERSON] et al. [PERSON] et al. (2018) presented a fusion score splicing and feature-matching method to solve the problem of 3D point-cloud stitching quality. However, their algorithm easily falls into local minima in certain complicated environments and does not provide the best stitching effect. [PERSON] et al. [PERSON] et al. (2018) used the point-cloud density feature to optimize both stitching accuracy and scene modeling accuracy, but the density model is sensitive to motion interference from dynamic participants in a scene, affecting the stitching accuracy. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) demonstrated the extraction of depth information from a specific image group and achieved higher precision stitching using depth and image information. However, the scene that is selected by the system is a specific scene, and so the system lacks generalization. [PERSON] et al. [PERSON] et al. (2018) proposed reconstruction of 3D point clouds using a continuous frame image and combining matching constraints and image feature scores to achieve high-precision scene stitching.