Rapid image registration method based on sub-image corner features

Abstract

The invention discloses a rapid image registration method based on sub-image corner features. The method includes the specific steps: firstly, selecting a reference sub-image and a to-be-registered sub-image, selecting one sub-image from a reference image as the reference sub-image, and selecting one sub-image with the same coordinate space as the reference sub-image from a to-be-registered image as the to-be-registered sub-image; secondly, extracting corners of the reference sub-image and the reference sub-image; thirdly, performing feature description on the corners extracted from the reference sub-image and the reference sub-image to obtain a feature vector of each corner; fourthly, performing similarity measurement and feature matching on the feature vectors of the corners on the reference sub-image and the reference sub-image to obtain K matching point pairs; and fifthly, adopting a least square method to compute a transformation matrix H between the reference image and the to-be-registered image based on the K matching point pairs, and registering the to-be-registered image onto the reference image based on the transformation matrix H. By the method, the requirement for image matching precision can be met, and image matching speed is increased greatly.

Description

technical field [0001] The invention belongs to the technical field of image registration, and in particular relates to a fast image registration method based on sub-image corner features. Background technique [0002] The application of image registration is very extensive, such as pattern recognition, automatic navigation, medical diagnosis, computer vision and other fields. At present, a lot of research work has been carried out in the aspect of image registration, and many image registration methods have been proposed. At present, most of the researches on image registration focus on feature extraction, feature description, similarity measurement, comparison of multiple registration methods, etc., but less attention is paid to the real-time registration. [0003] Common registration methods can be divided into two categories: feature-based registration methods, such as Harris corner method, SIFT method, etc.; region-based registration methods, such as mutual information...

AI Technical Summary

Problems solved by technology

However, this method involves time-consuming steps such as correlation coefficient calculation, wavelet coefficient subgraph selection, and iterative refinement, which makes the algorithm complex, difficult to implement, and poor in real-time performance.

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