Review of 2D Image Matching Algorithms Based on Deep Learning Features
The objective of image matching is to establish correspondences between similar structures across two or more images.This task is fundamental to computer vision,with applications in robotics,remote sensing,and autonomous driving.With the advancements in deep learning in recent years,Two-Dimensional(2D)image matching algorithms based on deep learning have seen regular improvements in feature extraction,description,and matching.The performance of these algorithms in terms of matching accuracy and robustness has surpassed that of traditional algorithms,leading to significant advancements.First,this study summarizes 2D image matching algorithms based on deep learning features from the past ten years and categorizes them into three types:two-stage image matching based on local features,image matching of joint detection and description,and image matching without feature detection.Second,the study details the development processes,classification methods,and performance evaluation metrics of these three categories and summarizes their advantages and limitations.Typical application scenarios of 2D image matching algorithms are then introduced,and the effects of research progress in 2D image matching on its application domains are analyzed.Finally,the study summarizes the development trends of 2D image matching algorithms and discusses future prospects.
国家科技期刊平台
NSTL
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