Abstract
This paper proposes a novel method for 2D-to-3D video conversion, based on boundary information to automatically generate the depth map. First, we use the Gaussian model to detect foreground objects and then separate the foreground and background. Second, we employ the superpixel algorithm to find the edge information. According to the superpixels, we will assign corresponding hierarchical depth value to initial depth map. From the result of depth value assignment, we detect the edges by Sobel edge detection with two thresholds to strengthen edge information. To identify the boundary pixels, we use a thinning algorithm to modify edge detection. Following these results, we assign the depth value of foreground to refine it. We use four kinds of scanning path for the entire image to create a more accurate depth map. After that, we have the final depth map. Finally, we utilize depth image-based rendering (DIBR) to synthesize left and right view images. After combining the depth map and the original 2D video, a vivid 3D video is produced.
Highlights
In the field of visual processing, 3D image processing has become very popular in recent years
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
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 taking advantage of this depth map to synthesize a 3D stereoscopic image
Summary
In the field of visual processing, 3D image processing has become very popular in recent years. In [12] they gave a semi-automatic method aimed to generate stereoscopic views estimating depth information from a single input video frame and to decrease computation resources. We use GMM (Gaussian mixture model) and SLIC (simple linear iterative clustering) to generate initial depth map and utilize edge information and repeat four kinds of scanning path mode to refine the depth value. The superpixels stage clusters pixels and assigns depth values based on edge information. We employ this algorithm to refine the edge information. Based on the derived depth map, two-view images are rendered by DIBR.
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