Super-pixel-level image global matching method

Abstract

A super-pixel-level image global matching method comprises the steps of obtaining an input image pair corrected by polar lines through a binocular stereo camera, calculating a self-adaption cross of each pixel of the input image pair so as to obtain a self-adaption window of the current pixel, calculating the matching cost of the pixel, adopting a replacement strategy to process a coverage area and adopting a suboptimum strategy to process an image boundary; establishing super-pixels for images, conducting planar fitting on a parallax value of each super-pixel area to determine reliable pixels and deleting obvious wrong planes so as to determine initial parallax plane set; calculating the matching cost of the super-pixels according to the obtained matching cost of the pixel, establishing a data item and a smoothing item and utilizing a Graph-Cut optimization algorithm to conduct continuous iteration on an energy equation so as to obtain a final parallax plane. The super-pixel-level image global matching method can effectively avoid the problem that image noise, distortion or pixel value abnormity and other situations easily occur in a weak texture area, a discontinuous-parallax area and the coverage area, is good in robustness and can obtain depth information more approximating to real scenes.

Description

Technical field [0001] The present invention relates to the technical fields of computer vision, image processing, etc., in particular to a stereo vision matching method based on a global optimization framework. Background technique [0002] The image global matching method is also called the stereo matching method based on the global optimization framework. It transforms the stereo matching problem into an optimization problem through modeling, and constructs a global optimization framework by establishing an energy function. Finally, under the global optimization framework, the optimization algorithm is used to solve the optimal solution of the energy function to obtain a global The optimal solution for stereo matching in the sense. At present, the more popular models for solving such problems are Markov random field model and Bayesian model and their derivative models; common optimization methods include graph cut algorithm and confidence propagation algorithm. Compared with...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the deficiencies of existing image global matching methods that are prone to noise, distortion or outliers in weak texture regions, discontinuous disparity regions, and occlusion regions, the present invention provides a method that can effectively avoid these situations, has good robustness, and can A superpixel-level image global matching method that obtains depth information closer to real scenes

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