Stereoscopic matching method on basis of variable-weight cost computation and S-census transformation

Abstract

The invention discloses a stereoscopic matching method on the basis of variable-weight cost computation and S-census transformation. The stereoscopic matching method includes carrying out census transformation by the aid of average values of the minimum uniformity of star neighborhoods instead of central pixel gray values; utilizing variable-weight Hamming distances as matching costs and acquiring initial disparity by the aid of non-local cost aggregation processes; further processing the initial disparity by the aid of disparity refining processes on the basis of average value shifting and processing incredible disparity regions to obtain high-precision disparity maps. The stereoscopic matching method has the advantages that the shortcomings of the traditional census transformation are analyzed, the variable-weight cost computation and the S-census transformation are proposed, and accordingly the anti-interference capacity of algorithms can be effectively enhanced; the matching accuracy can be effectively improved by selecting and analyzing processes with the matching costs which are the variable-weight hamming distances, and the mismatching rate can be reduced; the initial disparity can be acquired by the aid of the non-local cost aggregation processes, and accordingly the discrimination of disparity can be improved; the shortcoming of limitation such as low local stereoscopic matching precision and weak anti-interference capacity can be overcome, and high-precision stereoscopic matching can be implemented.

Description

technical field [0001] The invention relates to a stereo matching method, in particular to a stereo matching method based on variable weight cost calculation and S-census transformation, and belongs to the field of digital image processing. Background technique [0002] Stereo matching is one of the research difficulties and hotspots in the field of computer vision, and the matching accuracy has a significant impact on vision applications. At present, stereo vision has a wide range of applications in many fields such as industrial inspection, robot navigation, and military aviation surveying and mapping. [0003] According to different optimization theory methods, there are mainly two types of matching algorithms, local and global. The global algorithm obtains the matching result by minimizing the energy function, which has high precision and low efficiency. The local algorithm uses the neighborhood information in the window to perform single-pixel matching. It is fast an...

AI Technical Summary

Problems solved by technology

Suitable for stereo matching of real scene images, but the accuracy is not high

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