A Robust CPD Approach Based on Shape Context
Point matching is an important component of image registration.Recent years,Coherent Point Drift(CPD)method becomes a very popular point matching approach.CPD treats point matching as a probability estimation problem and speeds up the process of matching a lot.In this method,one set of points are thought to be sampled from a Gaussian Mixture Model(GMM),which is centered by the other set of points.However,CPD is sensitive to outliers and noises,especially when the noise ratio increased or the number of outliers gets much high.To deal with this problem,we introduce shape context into the step of searching for matching points and then improve the form of prior probabilities of GMM in this paper.The main idea of our method is that if the most points in a data set are likely to be matched to a particular centroid,this Gaussian component should be have a more influence to GMM.Therefore,we set prior probability of GMM with the similarity between GMM components and the data set.And the computation of similarity is based on shape context.The experiments on 2D and 3D images show that when noise ratio is low,our method performs as well as CPD does,but as the ratio increased,our method is more robust and satisfactory than CPD.
CPD,Shape context Image registration Point matching
YANG Yawen ZHANG Peng Peng QIAO Yu YANG Jie WANG Sheng Zheng
The Key Laboratory of Ministry of Education for System Control and Information Processing,Institute Merchant Marine College,Shanghai Maritime University,Shanghai 200240,China
国际会议
The 33th Chinese Control Conference第33届中国控制会议
南京
英文
4930-4935
2014-07-28（万方平台首次上网日期，不代表论文的发表时间）