Optimization Algorithm Based On Soft Registration Of Feature Points

With the development of artificial intelligence and deep learning,the field of computer vision,which is represented by image processing technology,has gradually become a research hotspot.In image processing,there is one key technology--image registration,as the back-end of the image processing flow.After a series of image preprocessing,such as feature extraction and detection,registration lays the foundation for the following applications such as identification,location and tracking,and the performance of the registration will directly affect the performance of the subsequent applications.Therefore,the significance of registration work can be seen.In this paper,the method of image registration based on feature points is studied.Dig deep the process of image registration,put forward and summary some optimization methods about important links and corresponding technologies.Make the registration based on feature point can achieve better results.This paper focuses on the following three problems,analyzes and gives the optimization scheme:In the traditional point registration method,the feature similarity is often used to solve the matching relation.In this paper,a new shape descriptor is proposed to describe feature points.The descriptor can establish the characteristics of isotropy and anisotropy of a feature point,and fuse the two features.This descriptor implements the "soft" of the allocation scheme,which makes the original hard feature division method more robust.According to the experiment,the proposed descriptor is better than the ICP(iterative closest point)algorithm and soft shape context algorithm in the case of severe deformation,noise and external points.Although the traditional ICP(iterative closest point)algorithm can realize rigid deformation registration in three-dimensional space,it must have a better initial value,so we put forward a solution of two alignment to fix this problem.The first alignment completes the centroid coincidence,ensuring the proximity of the two-point set,The second alignment uses a genetic algorithm to perform a global search in space,thus determining an optimal rotation matrix.After two initial alignment,the ICP provides a better initial position,and overcomes the shortcoming that the original scheme can not realize the registration under the conditionof too large angle.Experiments show that the ICP algorithm after two alignments can jump out of the local minimum point to achieve better matching.A robust non rigid registration algorithm(TPS-RPM)algorithm,realize the binary matching become “soft”.The method of continuous approximation optimization for a continuous matching matrix is implemented.The original algorithm reduces the matching matrix ambiguity with deterministic annealing,and finally achieves the registration task.In order to ensure the better matching results,the algorithm must use larger annealing rate,to repair this defect,the efficiency of the algorithm is improved by increasing the weights of the local similarity measures by updating the matching matrix,and the fast convergence of the induced matching matrix makes it possible to achieve better registration results at lower annealing rates.