Image matching method based on corner point and single-line segment marshalling feature description operator

Abstract

The invention discloses an image matching method based on a corner point and single-line segment marshalling feature description operator. The method comprises the following steps: firstly, extractingline segments and Harris corner points from an image; constructing a line segment, searching and grouping the line segment, constructing an angular point-single line segment texture descriptor with scale, rotation and illumination invariance by using a detected angular point and the line segment, the Harris angular point having the advantage of rotation invariance, and the line segment adopting ahalf-width descriptor can improve the reliability in a parallax change scene; performing spatial weighted shortest distance measurement to obtain a local matching result; finally, determining the candidate matching of each line segment, establishing a matching matrix and solving a global matching result through spectral analysis. The image matching description operator has the characteristics ofscale, rotation and illumination invariance. According to the invention, image pyramids are respectively established for three-dimensional images. The pyramids on different layers are matched one byone, so that the scale influence can be eliminated. The defects of large marshalling calculation amount and long consumed time in multi-line-segment matching can be overcome.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image recognition and computer vision, and relates to an image matching method, in particular to an image matching method based on a feature description operator grouped by Harris corner points and single line segments, and combined with geometric constraints to carry out a pyramid transfer strategy. Feature points and feature lines can be matched at the same time. Background technique [0002] Image matching technology has been widely used in 3D reconstruction, image retrieval, target tracking, military reconnaissance, etc., and has important application value in military, medical and ecological environment monitoring. Although the current feature matching algorithm has achieved remarkable results in the research based on point feature matching, there are still some problems in the matching technology based on line features due to factors such as illumination, noise, and occlusion: 1) Line...

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Problems solved by technology

Although the current feature matching algorithm has achieved remarkable results in the research based on point feature matching, there are still some problems in the matching technology based on line features due to factors such as illumination, noise, and occlusion: 1) Line segment features are not significant, Therefore, the significant SIFT operator will deliberately avoid the points on the line segment; 2) There are also problems in line segment matching based on geometric constraints, for example, the endpoints extracted by directly using the matching technology of epipolar line constraints are not accurate enough, and the viewing angle changes are required to be small or predictable. The geometric relationship between images; 3) The matching method based on line segment grouping usually includes the matching relationship between multiple internal line segments, and it is necessary to construct multiple possible feature groups, resulting in complex calculations and time-consuming; 4) The usual line segment feature descriptor will Make a buffer area on both sides of the line segment, and statistically describe the texture of the entire area. However, due to changes in the camera's viewing angle, the texture on one side of the line segment may be stable, and the texture on the other side will change greatly, resulting in matching uncertainty

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