基于局部特征匹配LoFLAT算法的研究与实现OA

In order to improve the accuracy and speed of the image registration algorithm,this study pro-poses a local feature matching method named LoFLAT,using a focused linear attention converter.LoFLAT consists of three main modules:a feature extraction module,a feature conversion module and a matching module.The feature extraction module utilizes ResNet and feature pyramid network to extract hierarchical features.The feature converter module refines the attention distribution using focused linear attention and the focused mapping function,and enhances feature diversity through deep convolution.The matching module predicts accurate and robust matching through a coarse-to-fine strategy.Experimen-tal results show that compared with SIFT,SURF,ORB and improved SIFT algorithms,LoFLAT algorithm extracts more and more accurate feature points in both texture-poor and texture-rich areas,while achieving higher matching speed.