基于改进网格运动统计特征的图像匹配算法

为了解决尺度不变特征变换(SIFT)算法在图像匹配中匹配正确率低、耗时长等问题,提出一种基于改进网格运动统计特征RANSAC-GMS的图像匹配算法。首先,利用快速旋转不变性特征(ORB)算法对图像进行预匹配,对预匹配的特征点采用网格运动统计(GMS)来支持估计量以实现正确匹配点与错误匹配点的区分;然后,采用改进的随机抽样一致性(RANSAC)算法通过匹配点间的距离相似性对特征点进行筛选,并采用评价函数对筛选后的新数据集进行重新整理,进而实现对误匹配点的剔除。采用Oxford标准图库和现实中拍摄的图像对图像匹配算法进行测试对比,实验结果表明,所提算法在图像匹配中的平均匹配正确率达到91%以上;与GMS、SIFT、ORB等算法相比,该改进算法的近景匹配正确率和远景匹配正确率分别最少提高了16.15个百分点和3.56个百分点,说明它能有效剔除误匹配点,进一步提高图像匹配精度。

Image matching algorithm based on improved RANSAC-GMS

In order to solve the problem that Scale Invariant Feature Transform (SIFT) algorithm has low matching accuracy and long time consuming in image matching, an improved image matching algorithm based on grid motion statistical feature, namely RANSAC-GMS, was proposed. Firstly, the image was pre-matched by Oriented FAST and Rotated BRIEF (ORB) algorithm and Grid-based Motion Statistics (GMS) was used to support the estimator to distinguish the correct matching points from the wrong matching points. Then, an improved RANdom SAmple Consensus (RANSAC) algorithm was used to filter the feature points according to the distance similarity between the matching points, and an evaluation function was used to reorganize the filtered new datasets to eliminate the mismatching points. The experiments were carried out on Oxford standard image library and images taken in reality. Experimental results show that the average matching accuracy of the proposed algorithm in image matching is over 91%. Compared with algorithms such as GMS, SIFT and ORB, the near-scene matching accuracy and the far-scene matching accuracy of the proposed algorithm are improved by 16.15 percentage points and 3.56 percentage points respectively. The proposed algorithm can effectively eliminate mismatching points and achieve further improvement of image matching accuracy.