Research On Non-rigid Image Registration Algorithms Based On Features

Image registration aims to search the spatial transformation relationship between two or more images,and then align these images in spatial position.As a research hotspot in the field of computer vision,non-rigid image registration has important theoretical significance and research value in the fields of medical image processing,remote sensing image analysis,image mosaic and fusion,and so on.It represents the study direction in the future.Firstly,this thesis analyzes the research background of non-rigid image registration and the research status at home and abroad.Then the non-rigid image registration methods based on features and optical flow estimation are emphasized,and their advantages and disadvantages are also pointed out.In addition,the subjective and objective evaluation criteria of image registration are given.Feature-based non-rigid image registration is prone to mismatching.To solve this problem,this thesis proposes a mismatch removal method based on super-pixel motion statistics.Firstly,the idea of super-pixel segmentation is introduced into feature matching,and an improved super-pixel segmentation algorithm is used to segment the images for registration.Secondly,this thesis proposes a consistency constraint of super-pixel motion by assuming that the feature points in the same super-pixel region are of the same or consistent motion trend.Finally,the super-pixel grids statistical model is established,and the local motion smoothing constraint is transformed into the statistical likelihood function of the matching number,thus the automatic identification and mismatch removal can be realized.The simulation results compared to the state-of-the-art algorithms shown that the proposed method is robust to the non-rigid images with large displacement deformation.In addition,this thesis proposes a non-rigid image registration algorithm based on vector field interpolation model.Firstly,motion boundary constraints are added to the feature vector field,and obtain the edge vector field on the moving boundary.After removing the mismatches,the precise sparse vector field is obtained.Then,a vector field interpolation modeling method is proposed,which can realize the interpolation of sparse vector field.Finally,the floating image is aligned according to the obtained dense displacement vector field,and then the final registration can be realized.This method can estimate the full-pixel motion vector field between non-rigid images using a small number of feature vectors,and then solve the problem of large displacement information missing in optical flow estimation algorithms.Multiple experimental results demonstrate the effectiveness and robustness of the proposed algorithm.Finally,the work in the thesis is summarized,and the study direction in the future is also expected.