Full-automatic foreground and background separation method based on binocular vision

Abstract

The present invention proposes a full-automatic foreground and background separation method based on the binocular vision. The method comprises: in the initialization phase, using the local matching algorithm to generate a disparity map preliminarily, then using the initial disparity map to generate a trimap, and according to a color linear based hypothesis theoretical method proposed by Levin and other people, solving the initial opacity alfa; in the iterative optimization phase, integrating the opacity information of the left and right images into the cost aggregation function to enhance the disparity map, especially the boundary region; using the enhanced disparity map to provide a more reliable trimap for matting, and using the disparity gradient as a diffusion map to insert into the matting formula; and carrying out constant iteration in the entire optimization process until a satisfactory result is obtained. The experimental results show that the proposed method has good robustness, significantly reduces the error at the boundary of the disparity map and improves the accuracy of the image matting.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, in particular to a fully automatic foreground and background separation method based on binocular vision. Background technique [0002] In the field of machine vision, binocular stereo vision technology has become more and more important, and it has been widely used in medical diagnosis, mobile phone photography, image tracking and other fields. People also have more and more stringent requirements on the quality of depth images, especially the edge parts of depth images. It is difficult to generate very accurate depth map boundaries in traditional stereo matching algorithms. Therefore, it is necessary to adopt a new method to make up for the shortcomings of the stereo matching algorithm at this point. [0003] A promising approach is to exploit the rich boundary details inherent in image matting. However, the existing classic matting algorithms all require the user to first s...

AI Technical Summary

Problems solved by technology

However, the existing classic matting algorithms require the user to first specify the foreground and background parts, which cannot realize automatic matting.

This greatly limits the application range of cutouts, such as applications on mobile phones

To make matters worse, this makes the quality of matting completely depend on the representativeness of the foreground and background specified by the user. If the user-specified certainty and background are not comprehensive, the matting effect will be greatly reduced

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