Image matching algorithm of bonding point characteristic and line characteristic

Abstract

The invention discloses an image matching algorithm of bonding point characteristic and line characteristic descriptors. The method comprises the following steps: (1) carrying out angle point extraction on a template drawing and a real-time drawing under multiscale; (2) acquiring an edge set surrounding angle points of the real-time drawing and the template drawing; (3) calculating a class ORB point characteristic descriptor of real-time drawing and template drawing angle points which are acquired from the step 1 and are selected finally; (4) using a minimum cut square Hausdorff distance to describe a matching similarity of the real-time drawing and template drawing edge set acquired from the step 2; (5) calculating a matching similarity of the class ORB point characteristic descriptor of the real-time drawing and template drawing angle points, wherein the characteristic descriptor is acquired from the step 3; (6) matching result integration. In the method of the invention, firstly, a stable point characteristic is used to carry out primary selection on the angle points so as to acquire a candidate point set and a correct position is included; then a global line characteristic is used to screen the candidate point set so that a repetition mode can be reduced and a correct rate is increased.

Description

technical field [0001] The invention belongs to the technical fields of image matching, computer vision and digital image processing, and in particular relates to an image matching algorithm combining point features and line features. Background technique [0002] With the rapid development of science and technology, especially computer technology, scene matching and recognition technology has become an extremely important and basic technology in the field of information processing, especially in the fields of aerospace, UAV navigation and missile terminal guidance. [0003] Scene matching and recognition is the process of matching and recognizing the real-time image taken by the aircraft on-site with the pre-prepared template image. The position of the aircraft is determined through image recognition, so that the heading deviation can be corrected. The currently used template images are mainly derived from satellite or aerial images, and the real-time images are the lower ...

AI Technical Summary

Problems solved by technology

Binary (multi-valued) templates are applied to the matching of complex scenes, which can partially eliminate the impact of adapting to grayscale changes in a statistical sense, but there will be more repetitive patterns and insufficient description capabilities.

Region-based matching recognition has a strong prior in the selection of regions, and it takes a long time to calculate when the template is large.

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