Stereo matching method based on image partitioning in three-dimensional television system

Abstract

The invention discloses a stereo matching method based on an image partitioning algorithm in a faster self-adaptive three-dimensional television system, aiming at obtaining partitioning information of a reference image and applying the partitioning information to the three-dimensional television system, wherein the method is better in instantaneity, accurate in obtaining depth information and strong in anti-interference ability for image noise (lighting, distortion and the like). The method comprises the following specific steps of: firstly, shooting left and right images using a parallel binocular camera and calibrating the images; secondly, calculating an initial parallax error by using an improved cross polymerization algorithm so as to obtain an initial parallax error image of a left image or a right image; thirdly, adopting the image partitioning algorithm based on a graph theory to partition the left image or the right image calibrated in the first step using the image partitioning algorithm based on the graph theory; and fourthly, carrying out median filtering to the initial parallax error image information obtained in the second step by using partitioned images so as to obtain a parallax error image of the left image or the right image needing to be matched.

Description

Technical field [0001] The invention relates to an image processing method, in particular to a stereo matching method based on image segmentation in a three-dimensional television system. Background technique [0002] Stereo matching is a core component of the computer vision field, and it has a wide range of applications in three-dimensional television, car assisted driving, computer vision and other practices. According to the different matching primitives, it can be roughly divided into feature-based matching algorithms and area-based matching algorithms. Although the feature-based stereo matching method is faster, it cannot obtain the global optimal disparity, so the global effect has been more recent in recent years. Good area-based matching algorithms are more widely used, among which graph-cuts, dynamic programming, and Belief propagation algorithms have greatly improved the accuracy and accuracy of stereo matching . [0003] Jiangbo Lu, Ke Zhang, Lafruit, G., Catthor, F. ...

AI Technical Summary

Problems solved by technology

Its disadvantage is that its segmentation assumption may not always be correct due to the influence of the segmentation algorithm, and due to the influence of forced smoothing, the obtained disparity may not represent the real disparity of the region

[0005] The mean-shift segmentation algorithm is a commonly used method to obtain image segmentation information. The mean-shift algorithm is a non-parametric estimation method that uses the gradient of the probability distribution to find the peak, and has high robustness. , it segments the image by clustering the color domain and spatial domain features of the input color image. This algorithm can get better segmentation results. The disadvantage of this algorithm is that the real-time performance is generally

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