摘要
网格运动统计(GMS)匹配算法中网格化图像加速了算法的实现,然而网格边缘的特征点没有进行有效地处理,导致匹配对中存在着错误匹配对,为此提出了一种融入自适应边距的网格运动统计的图像误匹配剔除算法。首先采用了自适应算法计算出最佳网格边缘距离,将网格边缘的特征点归属到相邻的其他网格,使得这些特征点可以有效发挥对正确匹配点的支持作用,提高了正确匹配点的得分;最后通过表征运动平滑约束的统计特性剔除初始匹配中的错误匹配点。仿真实验表明,该方法相比GMS算法召回率提高了10%左右,同时实时性也提高了30%左右,相比于SIFT算法,运行时间平均缩短了17倍;相比于SURF算法,正确匹配个数平均提高了8倍,充分说明能有效、高效地剔除错误匹配点,进一步提高图像匹配质量。
The grid-based image speeds up the implementation of the algorithm in the grid-based motion statistics(GMS) matching algorithm. However, the feature points at the edge of the grid are not effectively processed, which leads to the existence of wrong matching pairs. This paper proposes an image mismatching elimination algorithm based on adaptive margin mesh motion statistics. Firstly, the adaptive algorithm is used to calculate the optimal distance of the grid edge, and the feature points of the grid edge are assigned to other adjacent grids, so that these feature points can effectively play a supporting role for the correct matching points and improve the score of the correct matching points. Finally, the statistical characteristics representing the motion smoothing constraint were used to eliminate the wrong matching points in the initial matching. Simulation experiments show that the recall rate of the proposed method is about 10% higher than that of the GMS algorithm, and the real-time performance is also about 30% higher. Compared with the SIFT algorithm, the running time is shortened by 17 times on average. Compared with SURF algorithm, the number of correct matches is increased by 8 times on average, which fully indicates that the wrong matching points can be removed effectively and efficiently, and the image matching quality can be further improved.
作者
胡欣
胡陆明
刘归航
Hu Xin;Hu Luming;Liu Guihang(School of Electronics and Control Engineering,Chang'an University,Xi’an 719000,China)
出处
《电子测量技术》
北大核心
2021年第17期131-137,共7页
Electronic Measurement Technology
基金
陕西省科技计划项目(2019JQ-678)
国家重点研发计划项目(2019YFB1600800)资助。
关键词
图像匹配
去除误匹配
网格运动统计
算法融合
自适应边距
image matching
eliminating false matches
grid motion statistics
fusion algorithm
adaptive margin
