基于感知Hash和极线约束的改进AKAZE算法

针对图像在发生变化时特征点匹配准确率较低的问题, 提出一种基于感知Hash和极线约束的改进AKAZE（accelerated-KAZE）算法. 该算法将特征点匹配分为粗匹配和精匹配两个阶段, 粗匹配阶段利用特征点的最近邻次近邻比值和感知Hash算法进行匹配点对的筛选； 精匹配阶段使用随机抽样一致算法和极线约束进一步筛选匹配点对. 仿真实验结果表明, 与进行随机抽样一致算法剔除误匹配点对后的原算法相比, 特征点匹配准确率仍平均提高12.9%, 速度仅慢2.4%, 可在保证算法效率的前提下有效提升图像发生变化时匹配点对的准确率.

Improved AKAZE Algorithm Based on Perceptual Hash and Epipolar Constraint

Aiming at  the problem that the accuracy of feature point matching was low when  image changed, we proposed an improved AKAZE (accelerated-KAZE) algorithm based on perceptual Hash and epipolar constraint. The algorithm  divided feature point matching into two stages: rough matching and fine matching. In the rough matching stage,  ratio of the nearest neighbor and next nearest neighbor of feature points and the perceptual Hash algorithm were used to screen the matching pairs in  the fine matching stage,  the random sample consensus algorithm and epipolar constraint were used to further screen the matching pairs. The simulation results show that, compared with the original algorithm after the random sample consensus algorithm  eliminates the wrong matching pairs, the feature point matching accuracy is still improved by 12.9% on average, and the speed is only 2.4% slower, which can effectively improve the accuracy of matching pairs when the  image changes on the premise of  ensuring the efficiency of the algorithm.