山东理工大学学报（自然科学版）2026，Vol.40Issue(3)：45-49,5.
基于局部特征匹配LoFLAT算法的研究与实现
Research and implementation of LoFLAT algorithm based on local feature matching
王康涛 1钟桂娟 1黄萌萌 1刘晓芳 1姚海梅 1张涛2
作者信息
- 1. 温州商学院 信息工程学院,浙江 温州 325000
- 2. 淄博市教育服务中心,山东 淄博 255000
- 折叠
摘要
Abstract
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.关键词
特征点提取/SIFT算法/SURF算法/ORB算法/LoFLAT算法/图像配准Key words
feature point extraction/SIFT algorithm/SURF algorithm/ORB algorithm/LoFLAT algo-rithm/image registration分类
信息技术与安全科学引用本文复制引用
王康涛,钟桂娟,黄萌萌,刘晓芳,姚海梅,张涛..基于局部特征匹配LoFLAT算法的研究与实现[J].山东理工大学学报（自然科学版）,2026,40(3):45-49,5.
