A Progressive Stereo Matching Algorithm Based on Segment Matching and Bayesian Estimation

Abstract

The invention discloses a progressive type three-dimensional matching algorithm based on sectional matching and bayes estimation. The progressive type three-dimensional matching algorithm includes: 1) dividing an image into an edge area and a sectional area on the basis of responding of a Sobel filter, matching through a three-dimensional matching strategy based on a window and a sectional matching strategy, and combining to obtain a pre-matching depth image; 2) for invalid points in the pre-matching depth image, fitting a least square plane through effective points in a support window, estimating the depth of the invalid point position, and thickening the pre-matching image; 3) for the obtained pre-matching image, revising the depth of each point through a bayes maximum posterior probability method, considering using pre-matching values as the prior probability, and considering using smoothness of similarity and depth of the image as the posterior probability. By means of the progressive type three-dimensional matching algorithm, extraction of depth images from thick to dense and from coarse to fine can be finished through a progressive structure, and meanwhile, edge characteristics and smoothness are considered, so that the accurate and smooth depth images can be obtained.

Description

technical field [0001] The invention relates to a stereo matching method in the field of computer vision, in particular to a progressive stereo matching algorithm in the field of computer vision. Background technique [0002] Stereo matching technology is to find corresponding matching points from two or more images taken from different perspectives of the scene, so as to calculate the depth of each pixel in the image, which is an important part of stereo vision technology. Stereo matching is a hot and difficult point in current computer vision research, and it has been widely used in 3D reconstruction, 3D object modeling and recognition, and robot path planning. [0003] Stereo matching algorithms can be divided into global stereo matching algorithms and local stereo matching algorithms in terms of optimization methods. Among them, the global stereo matching algorithm calculates the depth map of the entire image by searching the global optimal solution of the entire image,...

AI Technical Summary

Problems solved by technology

Among them, the global stereo matching algorithm calculates the depth map of the entire image by searching the global optimal solution of the entire image, which has high accuracy, but has a large amount of calculation and cannot be calculated in real time.

The local stereo matching algorithm generally defines the evaluation function in the window near the pixel point to search for the local optimal solution, and the operation speed is fast, but in the case of unclear texture and object occlusion, there will be mismatching, and the resulting depth image is not smooth enough , the edge features cannot be kept intact, and more environmental information is lost

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