Image Matching Method Based on Variance Constraint Coupled Geometric Invariance
In response to the current problem of many image matching algorithms using distance meas-urement to achieve feature matching without considering the impact of image affine transformation,resul-ting in low accuracy of matching results,this paper proposes an image matching algorithm based on the centrosymmetric feature coupled affine measurement model.Firstly,the Forstner operator is introduced to extract feature points using the Robert gradient features of pixels;then,using the Haar wavelet information of the image and the grayscale values of the centrosymmetric pixels,the feature vector is obtained;next,u-sing the affine features of rotation,translation,and scaling between feature points,an affine metric model is constructed to calculate the matched feature point pairs;finally,the structural similarity index measure-ment(SSIM)function is used to calculate the structural similarity of matching point pairs,and to remove artifacts and truth from the matching point pairs in order to obtain the optimal matching effect.The exper-imental results show that compared with current matching methods,the proposed algorithm can not only achieve more accurate image matching,but also better adapt to the matching between images with affine transformation relationships.
万方数据
维普
