提出了一种新的基于条件数的图像配准算法。该方法在Harris算法提取角点的基础上，采用条件数定量地分析了噪声对确定图像间变换关系的影响程度，通过阈值设定筛选出具有良好稳定性的角点，克服了Harris角点检测可能存在的角点位置偏移和易受噪而提取出伪角点等问题。最后选择了Random Sample Consensus(RANSAC)匹配准则来确定匹配点对。经过实验证明了该配准算法具有精确性、抗噪性和鲁棒性。

In this paper, a novel Harris corner detection algorithm based on condition number is proposed. Firstly feature points are extracted using Harris corner detector which usually leads to either missing significant corners or detecting false corners due to noise. To overcome these drawbacks, condition number, which investigates the relationship between noise and the transformation of images, is used to screen out the steady points. Then a new match method called Random Sample Consensus(RANSAC) is applied to determine matching pairs. A number of experiments demonstrate the accuracy, efficiency and robust of the approach.