中文摘要：

针对当前图像匹配算法中匹配点提纯环节不能有效提取正确匹配点的问题,提出了多变换矩阵mRANSAC（multi-RANSAC）方法.由于数字图像离散采样的原因,匹配点不能准确对应,存在一定的误差,由其拟合出的变换矩阵也各不相同,因而一个变换矩阵不能包含所有的正确匹配点.通过对RANSAC的研究发现,在抽样计算结果非最大内点数组中,只要内点数足够多,也是正确的,这也可以通过不同图像匹配点数不同来客观印证.因而提出使用多变换矩阵增加匹配点数,提高提纯效率,并提出并集法、减集法、自适应内点数阈值法三种策略.结果表明,mRANSAC提纯结果比RANSAC方法多出60%～300%.通过对mRANSAC阈值的设置和调整,可以达到近似100%的提纯率.该方法也可应用到其他有类似提纯问题的领域中.

英文摘要：

To sove the problem that correct match points cannot be effectively extracted in the match point purification links of current image matching algorithms,mRANSAC（multi-RANSAC） multitransformation matrix method is proposed.Matching points cannot accurately correspond to each other due to the digital image＇s discrete sample style.There exist intrinsic position errors,and the corresponding transform matrices are different.Therefore,one transformation matrix cannot contain all the correctly matched points.The research results of RANSAC show that in the sets of non-maximal inner point number,enough points can induce correct matches,w hich can also be confirmed by the fact that different objective images result in different match numbers.Therefore,multi-transformation matrix is used to increase the matching point number and improve the purification efficiency.Three strategies,the set union method,the set extract method and the adaptive number threshold of inner point method are proposed.The purification results of mRANSAC are generally 60% to 300% more than those of RANSAC.Through setting suitable threshold value of mRANSAC,the purification rate can reach approximately 100%.This method can also be applied to solve the similar purification problem in other fields.