Image solid matching method and device thereof

Abstract

The invention relates to an image three-dimensional matching method and a device thereof. The invention is characterized in that the invention comprises the following steps: a graphic identifier is adopted to extract feature points of a detection image one and a candidate matching image two; a multi-dimensional vector identifier makes a multi-parameter description to the feature points of the feature list to acquire the multi-dimensional vectors described by the parameters of the feature points; a data optimizer reduces the multi-dimensional vector described by the parameters of each feature point of the remarkable feature point list of the image one, calculates the similarity of each feature point of the feature point list of the reduced image one and the feature point of the feature point list of the image two to acquire the candidate matching point set of the image one; a classifier is used to classify the comparison result to judge the matching result of the image one and the image two. The invention has higher matching accuracy and efficiency to the three-dimensional image matching.

Description

technical field [0001] The invention relates to an image stereo matching method and a device thereof. Background technique [0002] Stereo vision is the most important distance perception technology in the computer ranging method. It simulates the way of human visual processing of the scene, and can flexibly measure the stereo information of the scene under various conditions. Its role is irreplaceable by other computer vision methods. The most critical part in stereo vision is to match the corresponding points of multiple visual images, that is, stereo vision matching, referred to as stereo matching. Among them, the binocular stereo matching algorithm is the process of establishing a corresponding relationship between the matching points of two images, which is the key to the binocular stereo vision system. In fact, any 3D reconstruction system based on computer vision contains a matching algorithm as its core. The algorithm based on phase matching can avoid calculating th...

AI Technical Summary

Problems solved by technology

Therefore, the accuracy of matching is not high, resulting in more sparse stereo matching point pairs, and further processing is required to perform 3D reconstruction. For example, some methods solve the sparse problem of matching point pairs through the interpolation technology of stereo matching points

Although the interpolation technology can solve the problem of sparse matching points to a certain extent, the new matching point pairs obtained by interpolation are based on the interpolation data, and the error of the interpolation data leads to the mismatch of many point pairs.

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