Image local feature matching method and device based on non-geometrical constraint and terminal

Abstract

The invention provides an image local feature matching method and device based on non-geometrical constraint and a terminal. The method comprises steps of performing local feature detection on a target image and an image to be matched, to obtain local feature points of the images; performing local feature matching of the local feature points of the target image and the image to be matched, to obtain a plurality of first candidate matching feature points and a plurality of second candidate matching feature points, wherein each first candidate matching feature point matches a corresponding second candidate matching feature point, the first candidate matching feature point is located in the target image, and the second candidate matching feature point is located in the image to be matched; and performing neighbor constraint detection on the plurality of first candidate matching feature points and the plurality of second candidate matching feature points, and adding the first candidate matching feature points in accordance with the neighborhood constraint and the correspondingly-matched second candidate matching feature points to a matching feature point set. The technical scheme of the invention is advantageous in that, elimination of mismatching in types of images is effectively achieved.

Description

technical field [0001] The present invention relates to the field of image processing, in particular to an image local feature matching method, device and terminal based on non-geometric constraints. Background technique [0002] In the field of computer vision, local features of an image refer to the features of the local area of ​​the image; local usually refers to the pixel area that can appear stably and has good distinguishability. The local feature matching method has been widely used in many problems in the field of computer vision, and has become a necessary means in image retrieval, target detection and recognition and other application fields. [0003] In the prior art, local feature matching is divided into two stages: feature vector matching and mismatch elimination. Feature vector matching refers to the process of calculating feature vectors of feature points through descriptors, and then finding matching feature points according to the similarity between featu...

AI Technical Summary

Problems solved by technology

Since the non-rigid target can be deformed, the affine transformation relationship of the non-rigid target between the two images cannot be determined at this time, and it is difficult to eliminate the mismatch through geometric constraints, resulting in low target detection accuracy

In addition, when the relationship between two images is not a simple affine transformation, such as when the image contains repeated targets and multiple rigid targets, the RANSAC algorithm cannot eliminate the mismatch, which reduces the target detection rate

Images