基于SIFT算子融合最大相异系数的自适应图像匹配算法

针对传统的尺度不变特征变换（SIFT）图像匹配算法存在的误匹配率较高、剔除误匹配点条件单一的问题，提出一种基于SIFT算子融合最大相异系数的自适应图像匹配方法。首先，在欧氏距离（Euclidean distance）比测度基础上，对SIFT算法中128维特征向量自适应获取最大相异系数优化；然后，确定最大相异系数最优取值进行匹配点筛选，并采用随机抽样一致性（RANSAC）算法进行匹配正确率计算；最后，利用Daniel Scharstein和Richard Szeliski立体匹配图像进行了算法验证。实验结果表明，改进算法较传统SIFT算法匹配正确率提升10个百分点左右，有效降低误匹配，更能够适应相似区域较多的图像匹配应用。在实时性上，所提方法单次匹配平均耗时1.236 s，可应用于实时性要求不高的系统。

Adaptive image matching algorithm based on SIFT operator fused with maximum dissimilarity coefficient

As the traditional Scale Invariant Feature Transform (SIFT) image matching algorithm has high false matching rate and eliminating the condition of mismatching points is unitary, an adaptive image matching method based on SIFT operator fused with maximum dissimilarity coefficient was proposed. Firstly, On the basis of Euclidean distance measurement, the optimal maximum dissimilarity coefficients values of the 128-dimensional feature vectors in SIFT algorithm were obtained. Then, the matching points were selected according to the obtained optimal values. Random Sample Consensus (RANSAC) was used to calculate the correct rate of matching. Finally, the stereo matching images of Daniel Scharstein and Richard Szeliski were used to verify the algorithm. The experimental results show that the correct matching rate of the improved algorithm is about 10 percentage points higher than that of the traditional SIFT algorithm. The improved algorithm effectively reduces the mismatches and is more suitable for image matching applications with similar regions. In terms of runtime, the proposed method has an average time of 1.236 s, which can be applied to the systems with low real-time requirements.