Edge-based parallax image calculation methods and devices applied to binocular stereo vision

Abstract

The invention relates to an edge-based parallax image calculation method applied to binocular stereo vision. The method includes: acquiring gradient images of a scene on the basis of depth images, shot by two depth cameras, of the same scene; acquiring a minimum matching cost value and a second minimum matching cost value of first pixel points in the gradient images according to a difference cost function; and determining that parallax of the first pixel point is invalid parallax if a ratio of the minimum matching cost value to the second minimum matching cost value is greater than a preset ratio. According to the embodiment of the invention, a low texture region or a repeated texture region corresponding to the first pixel point can be determined in the case where the ratio of the minimum matching cost value to the second minimum matching cost value is greater than the preset ratio, then it is determined that the parallax of the first pixel point is the invalid parallax, thus calculating parallax for pixel points outside high-level texture regions can be reduced, accurately calculate the parallax is more facilitated, and then an edge-based stereo matching parallax image is output on the basis of the parallax.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an edge-based disparity map calculation method applied to binocular stereo vision and an edge-based disparity map calculation device applied to binocular stereo vision. Background technique [0002] The stereo matching disparity image currently used for analysis and calculation, the main one is the side-based stereo matching disparity image. In order to determine an edge-based stereo matching parallax image according to the parallax image, it is necessary to accurately determine the parallax of each pixel in the parallax image, so as to accurately determine the edge in the parallax image through filtering processing. [0003] However, in the current image, there are many areas where it is difficult to accurately determine the parallax of pixels, such as low-textured areas, repeated texture areas, and occluded areas. image, it is difficult to accurately determine...

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Problems solved by technology

[0003] However, in the current image, there are many areas where it is difficult to accurately determine the parallax of pixels, such as low-textured areas, repeated texture areas, and occluded areas. image, it is difficult to accurately determine the edges in the disparity image, and the generated edge-based stereo matching disparity image is also difficult to meet the requirements

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