中文摘要：

传统的匹配点对提纯算法通常需要寻找小部分点集作为初始输入，再迭代求解出能够满足大多数点对约束要求的最优解，其提纯结果易陷入局部极值，造成正确匹配点对的遗漏。针对这一问题，本文引入了主成分分析思想，将整体点集作为初始输入，逐步剔除误匹配点对，稳健求解，得到更为准确的全局最优解，降低正确匹配点对的遗漏率，达到较好的提纯效果。试验表明，本文方法在一定的原始误匹配率下，能够得到整体最优解，在剔除误匹配点对的同时，能够避免或减少正确匹配点对的遗漏。

英文摘要：

The traditional purification method of matching points usually uses a small number of the points as initial input. Though it can meet most of the requirements of point constraints, the iterative purification solution is easy to fall into local extreme, which results in the missing of correct matching points. To solve this problem, we introduce the principal component analysis method to use the whole point set as initial input. And thorough mismatching points step eliminating and robust solving, more accurate global optimal solution, which intends to reduce the omission rate of correct matching points and thus reaches better purification effect, can be obtained. Experimental results show that this method can obtain the global optimal solution under a certain original false matching rate, and can decrease or avoid the omission of correct matching points.