![]() In automatic methods, human-computer interactions are not involved. According to whether human intervention, we can divide 2D to 3D image synthesis in two ways: automatic and semi-automatic. Thus, the quality of the depth map largely affects the quality of the 3D image. Synthesis technology from a 2D image to a 3D image is performed in two steps: an estimation of the original 2D image depth map and then taking advantage of this depth map to synthesize a 3D stereoscopic image. The lack of 3D videos makes 2D-to-3D image conversion quite practical. The user only watches 3D animation or 3D movies made by a special camera on a computer. To produce a better display than the traditional 2D visual experience, 3D displays offer a number of new applications, including education, games, movies, cameras, etc., with 3D video generations still growing. In the field of visual processing, 3D image processing has become very popular in recent years. After combining the depth map and the original 2D video, a vivid 3D video is produced. Finally, we utilize depth image-based rendering (DIBR) to synthesize left and right view images. We use four kinds of scanning path for the entire image to create a more accurate depth map. ![]() Following these results, we assign the depth value of foreground to refine it. To identify the boundary pixels, we use a thinning algorithm to modify edge detection. ![]() From the result of depth value assignment, we detect the edges by Sobel edge detection with two thresholds to strengthen edge information. According to the superpixels, we will assign corresponding hierarchical depth value to initial depth map. Second, we employ the superpixel algorithm to find the edge information. First, we use the Gaussian model to detect foreground objects and then separate the foreground and background. This paper proposes a novel method for 2D-to-3D video conversion, based on boundary information to automatically generate the depth map. ![]()
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