In this paper, an affine invariant feature matching algorithm is based on sparse features into dense features is proposed, in which sparse features include coordinates, scale, affine parameters etc., dense features include information of Gaussian kernel, area descriptor. Based on the Affine-SIFT algorithm, this algorithm improves the shortcomings of sparse feature extraction in the feature extraction phase. Because the dense information can only extract the feature when the sparse parameter is full of enough detection conditions, it can not match the characteristic (including sparse and dense parameters) that can be matched, in this paper, we will reconstruct the sparse features by using the sparse features to construct the new simulation images, and further extract the sparse features on the basis of the simulated images, and can detect the matching features that can not be detected in the original image, Feature set the probability of matching, to improve the correct match the number of goals. Compared with the ASIFT, the algorithm, in this paper, which can significantly increase the number of correct feature matching points is proved by experiments. In addition to the extending method of the ASIFT. The proposed method can also be used to extend other feature extraction and matching methods with sufficiently parameters of sparse feature, and apply to precisely target recognition, target classification and 3D reconstruction etc.
