基于改进SURF的增强现实图像匹配方法OACSTPCD

Aiming at the problems of poor robustness and efficiency of traditional augmented reality image matching algorithms,an improved SURF matching algorithm is proposed.Firstly,the SURF algorithm is used to detect the feature points,then the Haar wavelet template is used to determine the main direction of the feature,and then the feature descriptor is constructed after the main direction of the feature is obtained.Because the traditional SURF algorithm uses rectangular descriptors up to 64 dimensions,which is computationally in-tensive and robust.Therefore,the DAISY circle descriptor is used instead of the rectangle descriptor in the original algorithm.DAISY is a three-layer concentric circle structure,each layer contains 8 sampling points,which can obtain 25 dimensional descriptors.This structure greatly enhances the robustness of the algorithm and reduces the computational complexity.Then Euclidean distance is computed using the feature descriptor for feature point matching.Finally,the resulting set of matching points is optimized using random sampling consistency(RANSAC)and triangular irregular network(TIN)to eliminate mismatched points.The experimental results show that the time complexity of the proposed algorithm is slightly increased,the robustness becomes stronger,and the efficiency and matching accuracy of the proposed algorithm are also greatly improved,with the average accuracy reaching more than 95%.