A Surface Matching Algorithm Based On The Feature

In the field of computer vision,surface matching is a very important basic research,and widely applied in three-dimensional recognition,reverse engineering and other fields.The main research direction of this paper is surface matching algorithm based on the feature.Its core processing process include three steps: feature point detection,feature description and feature matching.In view of these three steps,a multi-scale feature descriptor based on covariance matrix is proposed to describe feature points.The correlation downsampling method is introduced into the proposed feature descriptor to detect feature points.At the same time,an error matching elimination algorithm based on evolutionary game theory is proposed to improve the performance of feature point matching.Specifically,the following aspects of work have been done.(1)A multi-scale feature descriptor based on covariance matrix combining three-dimensional geometric information and visual information is proposed.The descriptor constructs a multi-scale covariance matrix by using the color and robust geometric features of the feature points and their neighborhoods to describe the feature points.It can solve the problem of poor noise resistance caused by the current feature descriptor only focusing on the description of geometric information of the feature points.The experimental results show that when matching complex three-dimensional models,this feature descriptor is more robust to the noise and the change of resolution than other feature descriptors.(2)A feature point detection algorithm based on correlation downsampling is proposed.The correlation region is divided by calculating the correlation degree of the candidate feature points,and takes the points whose saliency value is the maximum as the feature points in the region,the de-sampling of the candidate feature points is completed,which can solve the clustering phenomenon of the current feature points,and the inefficiency of the current detected algorithm.The experimental results show that this feature detection algorithm can solve the clustering phenomenon of feature points without affecting the matching accuracy,and the number of points involved in later feature matching is reduced more than 70%,which improves the efficiency of the algorithm.(3)An error matching elimination algorithm based on evolutionary game theory is proposed.Firstly,the algorithm constructs the payoff matrix by calculating the similarity,distance and angle consistency of the feature descriptors between any two pairs of initial correspondence relationships,and calculates the overall average payoff.Then,the evolutionary process is dynamically simulated according to the infection and immune dynamics equation,the initial correspondence which obtains larger payoff survives while the initial correspondence which obtains smaller payoff is eliminated,and the final evolutionary equilibrium state is gradually iterated,and the final evolutionary equilibrium is iterated step by step.Finally,the remaining initial correspondence in the evolutionary equilibrium is the final matching pair,which achieves the elimination of error correspondence.The experimental results show that the algorithm has the advantages of high efficiency and strong anti-noise.