SIFT-based image registration method, device and system and storage medium

Abstract

The invention discloses an SIFT-based image registration method, device and system and a storage medium, and the method comprises the steps: obtaining a to-be-registered image and a reference image, carrying out the preprocessing of the to-be-registered image, and constructing a Gaussian difference scale space; extreme points in different scale spaces in the Gaussian difference scale space are obtained, feature points are selected according to the extreme points, and feature descriptors are generated for the feature points; calculating a local information entropy of the feature point field, and determining a final feature descriptor of the feature point according to the local information entropy and the feature descriptor; and optimizing feature point matching according to a genetic algorithm, generating a matching point pair, and correcting the matching point pair to complete image registration of the to-be-registered image. According to the method, the number of feature points needing to be matched and the areas needing to be matched are reduced, the matching efficiency and the algorithm speed are optimized, and the search efficiency and the registration precision of the SIFT algorithm are improved.

Description

[0001] Technology neighborhood [0002] The present invention relates to the field of image processing technology, and more particularly, to an image registration method, device, system and storage medium based on SIFT. Background technique [0003] The SIFT algorithm has good stability and is widely used in feature-based image matching algorithms. The SIFT descriptor pair improves the image scale invariance and rotation invariance of the algorithm on the basis of the original algorithm, and the features have relatively high performance. High uniqueness can maintain good matching robustness under different viewing angles, different shooting distances, different affine transformations, and different lighting conditions. [0004] However, the traditional SIFT algorithm still has defects such as high computational complexity, long matching time, unable to calculate in real time, and unable to meet people's growing demand for matching accuracy, and the anti-noise still needs to be...

AI Technical Summary

Problems solved by technology

[0004] However, the traditional SIFT algorithm still has defects such as high computational complexity, long matching time, incapable of real-time calculation, and inability to meet people's growing demand for matching accuracy, and the noise resistance still needs to be strengthened. Therefore, reducing the complexity of the algorithm Improving the operation speed of the algorithm, improving the matching accuracy and noise resistance of the algorithm, etc., to improve and optimize the SIFT algorithm has become the current mainstream research direction.

[0005] On the basis of the existing theory, aiming at the problems of high computational complexity, long matching time, and inability to calculate in real time in the SIFT algorithm, an improved algorithm is proposed, by reducing the number of feature points that need to be matched and the number of points that need to be matched. Region and descriptor vector dimensions to improve matching efficiency and algorithm speed

And on the basis of the existing theory, aiming at the shortcomings of the SIFT algorithm, such as low precision and still need to strengthen the noise resistance, put forward the corresponding improvement plan to make it more suitable for the needs of today's society

Images