已知最大定位误差的点特征松弛匹配

研究了在已知最大定位误差的情况下时，如何对基本点特征松弛匹配算法进行改进，以提高匹配性能。基本思想是利用已知的最大定位误差，确定两点对的最小匹配度量，从而减小虚假特征点对匹配结果的影响。采用针对模拟图像及实际卫片航片图像对的实验，比较了两种方法的性能。结果表明，在已知最大定位误差的情况下，该改进方法比基本点特征松弛匹配算法具有更好的匹配性能。

Relaxation Matching Based on Point Features With Known Error Bounds

When the error bound of location deviation is known, the original point pattern relaxation matching algorithm is improved to obtain better matching performance. The known error bound of location deviation is used to determine the minimum matching merit for two point pairs so that the effect of spurious feature points can be reduced. Experimental results with large simulated images are given to compare the performance of the original method and algorithm proposed in this paper. It was shown that with known error bound of location deviation, better matching performance could be obtained by the proposed algorithm relative to the original point pattern relaxation matching technique.