Research on Wide Baseline Weak Texture Image Matching Based on Deep Learning
In order to solve the challenging problem of wide baseline weak texture image matching,this paper delves into deep learning based matching methods.Firstly,a complete process for matching wide baseline weak texture images was designed,including key steps such as geometric correction,coarse level matching prediction,and final matching.In terms of geometric correction,this article adopts an effective image registration method to correct geometric distortions caused by photography angles and terrain changes.Subsequently,by introducing deep learning models,coarse level matching prediction for wide baseline weak texture images was achieved,providing reliable initial values for subsequent fine matching.In the final matching stage,this paper proposes a comprehensive matching algorithm that combines traditional features and deep learning features.The research results show that when using the method proposed in this paper for wide baseline weak texture image matching,the matching accuracy is above 95.00％,demonstrating the superior performance of this method in processing challenging image data.
万方数据
维普
