Similarity measure function-based improved local stereo matching method

Abstract

The invention belongs to the field of stereo matching of images, and aims at disclosing a new local stereo matching framework so as to achieve the aims of rapidly and correctly obtaining stereo image disparity maps. The technical scheme of the method is a similarity measure function-based improved local stereo matching method which comprises the following steps of: constructing a new similarity measure function by combining a Census transform method according to space distance information among a color, a gradient and pixels of a to-be-matched image, and calculating a matching cost of each pixel; calculating the sum of the matching cost of each point, namely, carrying out cost aggregation, and in the cost aggregation process, obtaining an initial disparity map through a guide map filter method by adoption of a cross frame-based self-adaptive window; and carrying out image segmentation on the initial disparity map by utilizing a mean shift algorithm, and optimizing mistaken matching points in the disparity map through a surface interpolation method by combining a credibility distribution map of images, so as to obtain a final parallax result. The method is mainly applied to the stereo matching occasions of images.

Description

technical field [0001] The invention belongs to the field of image stereo matching and relates to the improvement and optimization of a stereo matching algorithm. Specifically, it involves an improved local stereo matching method based on the similarity measure function. Background technique [0002] Stereo vision is a technology based on the principle of stereo imaging, using computer two-dimensional image processing technology to restore the spatial information in the three-dimensional scene from the two-dimensional image. The research on this technology originated in the 1860s by Robert et al. [1] The computer vision system simulation experiment carried out, they used multiple polyhedrons of different shapes, and studied the computer system's ability to recognize and locate objects in real scenes by identifying the positions and relative distances of polyhedron points, lines, and surfaces. This is the first transformation of spatial information technology from the field ...

AI Technical Summary

Problems solved by technology

Compared with the local algorithm, the disparity map obtained by the global stereo matching algorithm is more accurate, which can effectively solve the problems encountered by the local algorithm. However, the global algorithm has high computational complexity, long calculation time, and requires a large number of artificial setting parameters. Suitable for applications with high real-time requirements

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