Method for improving sparse corresponding point image matching accuracy and correcting stereo image

Abstract

The invention discloses a method for improving sparse corresponding point image matching accuracy and correcting a stereo image. The method comprises the steps of (1) improving sparse corresponding point image matching accuracy, namely amplifying local image diamonds and finding the smallest SSD value and corresponding central point coordinates by means of the blocky SSD matching method; (2) correcting the stereo image, namely preprocessing the corresponding point coordinates, resolving the last six coefficients in an Hg matrix by means of the SVD method and filling in the first three coefficients, carrying out reversal preprocessing on the Hg matrix to obtain an H matrix, and correcting one image to enable only horizontal parallax to exist between the image and the other image by means of the H matrix. The method for improving the sparse corresponding point image matching accuracy and correcting the stereo image has the advantages that the sparse corresponding point image matching accuracy is improved, searching time can not be increased obviously, the implementation process for stereo image correction is simple, calibration is not needed, corresponding points do not need to be on the same plane, and searching time in a dense corresponding point matching process is shortened.

Description

technical field [0001] The invention relates to an image processing and computer vision technology, in particular to a method for improving the matching accuracy of sparse corresponding point images and correcting stereoscopic images. Background technique [0002] For the sparse feature point matching of two images, there are mainly two solutions: 1. Manually find corresponding points and perform matching; 2. Use corner point detection algorithms such as Harris and SUSAN or automatic feature point matching algorithms such as SIFT and SURF to find corresponding points. Click and match. [0003] In the field of computer vision, stereoscopic image correction is also called epipolar line correction. One (or two) images are corrected so that all corresponding points of the two images only have horizontal parallax, which reduces the search time in the process of matching dense corresponding points. Mainly There are three solutions: 1. Calibrate the camera, use the combination of ...

AI Technical Summary

Problems solved by technology

[0004] In fact, the original sparse corresponding point matching method may have artificial matching + 2 The accuracy of the pixel error is not high enough and there is a problem of mismatching when automatic feature point matching

The original part of the epipolar line correction method has the problem of requiring the camera to be calibrated or the corresponding points to be on the same plane

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