改进FAST和对立颜色特征的向量场一致性匹配

针对目前传统图像匹配算法在复杂环境下存在误匹配点对过多、稳健性较差等问题，本文提出一种基于改进FAST的特征点提取，结合对立颜色特征的图像匹配算法。首先，利用改进FAST算法提取的角点作为特征点，结合改进的Opponent SIFT算法对特征点进行描述；然后，使用基于字符定位算法对提取的特征点对进行粗匹配，降低整体匹配过程中特征点对误匹配的风险。最后，为了规避因RANSAC算法易陷入局部最优解而导致正确点对被误剔除的问题，运用向量场一致性替代RANSAC进行提纯，降低误匹配率。通过对比试验表明，改进算法匹配准确率均大于91%，且对差异变化具有较好的稳健性、适应性。

The vector field consistent matching of FAST and opposite color features is improved

In order to solve the problemswith traditional image matching algorithms, such as too many false matching point pairs, and poor robustness in complex environment, an image matching algorithm is proposed based on improved FAST to extract feature points, combing with opposite color features.First, the corner points extracted by the improved FAST algorithm are used as feature points, and the feature points are described by combining with the improved Opponent SIFT algorithm. Then, the extracted feature point pairs are roughly matched based on the character location algorithm, to reduce the risk of false matchingof the feature point pairs in the whole matching process. Finally, in order to avoid the problem that the correct point pairs are mistakenly removed caused by the RANSAC algorithm easily falling into the local optimal solution, the vector field consistency is used instead of RANSAC for purification of matched point pairs to reduce the false matching rate. The comparison experiments show that the accuracy of the improved algorithm is greater than 91%, and it has good robustness and adaptability for the variation of differences.