Binocular stereo matching method based on edge information

Abstract

The invention discloses a binocular stereo matching method based on edge information. Firstly, performing color conversion on a left image and a right image acquired by Gaussian sampling through a Gaussian color model, introducing gradient information, using a neighborhood median as a threshold to replace a central pixel of a window, and counting the number of different corresponding bits in a Census transform value by using a Hamming distance so as to obtain a matching cost amount; secondly, converting the acquired image into an undirected graph, performing gradient operation by using a weighting function, constructing a minimum spanning tree, and performing cost aggregation by combining a cross-scale cost aggregation method to obtain a parallax value; generating a disparity map accordingto the obtained disparity value by using a winner-king strategy; and finally, carrying out region division on the pixel points by utilizing a super-pixel segmentation algorithm, and carrying out optimization processing on the disparity map by combining weighted median filtering, so that disparity information with relatively high precision can be obtained, and particularly, a relatively accurate disparity map can be obtained in an occlusion region and an edge information discontinuous region.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a binocular stereo matching method based on edge information. Background technique [0002] Computer Vision (Computer Vision) refers to the use of computers and related equipment to simulate human vision and then identify, track, measure, etc. machine vision. Its main task is to process the captured pictures or videos to obtain 3D information of the corresponding scene, just like what human eyes do every day. Binocular stereo vision is an important branch of computer vision. Binocular vision simulates the principle of human vision and uses the method of passive perception of distance by computer. Observe an object from two or more points, obtain images under different viewing angles, and calculate the offset between pixels through the principle of triangulation according to the matching relationship between pixels in the images to obtain the three-dimensional information...

AI Technical Summary

Problems solved by technology

The operation speed of the local algorithm is fast, and the disparity of the rich texture area can be quickly calculated, but there will be a mismatch in the low texture area, and the disparity map is not close, so it needs to be corrected by the interpolation algorithm in the later stage

The global algorithm can basically obtain all the disparity information to generate a dense disparity map, but it takes a long time to calculate and will also cause mismatching

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