Image Matching Method Based on Laplacian Feature Constrained Coupling Variance Measure

Abstract

Abstract In order to overcome the current image matching algorithms, which mainly use the distance information between pixels to achieve feature matching, ignoring the variance information between images, resulting in more false matching in the matching results, this paper designs an image matching method based on Laplacian feature constrained coupling variance measure. Firstly, Harris operator is introduced to extract image features roughly. On the basis of rough extraction, Laplacian feature of pixels is used to optimize the extracted image features to obtain more accurate image features. Then, the gradient feature of the image is used to calculate the direction information of the image. Based on the gradient feature, the neighborhood of the feature points is established, and the Haar wavelet value in the neighborhood is obtained to obtain the feature vector. Finally, the regional variance model is used to measure the variance information of the image, and it is introduced into the process of image feature matching. The variance information is added on the basis of Euclidean distance measurement of feature points to achieve image feature matching more accurately. RANSAC method is used to purify the results of feature matching, eliminate mismatching and complete image matching. The experimental results show that compared with the existing matching algorithms, the proposed algorithm has better matching performance and higher accuracy, which accuracy maintained above 90%.