## 1 Introduction  
Object segmentation separates the foreground objects in an image from the background image. Subsequently, object labeling is performed to record the label number, position, and region of each foreground object. Relevant labeling information can be used for subsequent object recognition and tracking. Currently, object segmentation and recognition techniques have been applied to various systems, such as robot vision [1, 2], autonomous driving [3, 4], intelligent monitoring [5, 6], and unmanned vehicles [7, 8]. Object segmentation can be divided into two categories, dynamic and static image segmentation. In terms of dynamic image segmentation, objects are segmented using the image continuity characteristic of consecutive images. [PERSON] et al. [9] proposed information-entropic variables, Point Divergence Gain, Point Divergence Gain Entropy, and Point Divergence Gain Entropy Density, to characterize the dynamic changes in image series. The information-entropic variables can be used to detect and segment moving objects. [PERSON] [10] used the motion changes of consecutive and adjacent images to segment foreground objects. The background subtraction algorithm [11] can accomplish object segmentation by comparing the difference between the background image and the input image. [PERSON] et al. [12] used the background subtraction algorithm based on the probability change of pixels in consecutive images to segment foreground objects. In contrast to dynamic image segmentation for consecutive images, static image segmentation involves analyzing the characteristics of the image itself to achieve object segmentation. Static image segmentation typically uses the gray level, color, or edge information of a single image to complete object segmentation. [PERSON] et al. [13] used the gray-level histogram of the image to conduct multilevel thresholding analysis and multilevel thresholds to segment objects of different gray levels. Color segmentation [14; 15; 16] is a process in which pixels of different colors are divided into different categories and objects by color clustering. Because colors are relatively sensitive to changes in light, images are often converted from RGB to other color spaces, such as HSI, CIELAB, and CMYK, to obtain better results. Contour features [17; 18] use the shape and surface texture as a basis for object segmentation. Object characteristics change drastically near edges and are not easily affected by changes in color or light. Compared with the color segmentation and background subtraction algorithm, contour features are more stable and less restrictive. Therefore, contour features are the information commonly used in most object segmentation studies. Owing to a lack of information regarding consecutive images, static image segmentation is much more difficult than dynamic image segmentation in terms of object segmentation. Both dynamic and static object segmentation techniques involve the segmentation of overlapping objects in two-dimensional (2D) images. Although dynamic images can use the object movement information to segment the objects moving in different directions, they cannot segment the overlapping objects moving in the same direction. Existing studies have used three-dimensional (3D) information to segment overlapping objects in pair images. Object segmentation techniques based on 3D information can also fall into two types: dynamic and static. For dynamic 3D image segmentation, [PERSON] et al. [19] extracted keyframes at fixed intervals in consecutive images of RGB-D video and used image and depth information for each keyframe to complete the object segmentation. Although the method can effectively segment complex overlapping objects, it relies on object recognition to segment-specific objects. Considering that the change in motion of moving objects in consecutive images is larger than that of the background image, [PERSON] et al. [20] combined long-term motion and stereo information and used stereoscopic foreground trajectories to segment the moving objects. [PERSON] et al.