Image Registration Method Based on Improved SIFT Algorithm and Essential Matrix Estimation

In this paper, we propose an image registration algorithm based on improved SIFT (Scale-Invariant Feature Transform) algorithm and essential matrix estimation based on RANSAC (Random Sample Consensus) and AC-RANSAC (A Contrario RANSAC) algorithm. So that in the 3D reconstruction, we can directly restore the parameters of the camera by using the essential matrix model estimated by image registration algorithm. The essential matrix is a 5-parameter model, reflecting the relationship between the representation of the spatial image points in the camera coordinate system under different viewing angles. SIFT algorithm not only maintains the invariance of scale, rotation, brightness and so on, but also maintains a certain degree of stability to the angle change, affine transformation and noise, but the time performance is low and the matching accuracy is not high enough. Therefore, we propose to narrow the dimension of the SIFT feature vector to reduce the time consumption, and increase the similarity measure of the nearest neighbor distance less than 0.3 to calculate the feature point correspondence between images. The experimental results have demonstrated that our method not only can guarantee better time performance, but also can effectively eliminate the wrong match point, greatly improving the matching accuracy.