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    江一苇, 顾幸生. 基于网格加速与顺序选取策略的图像匹配算法[J]. 华东理工大学学报(自然科学版), 2022, 48(5): 657-664. DOI: 10.14135/j.cnki.1006-3080.20210401002
    引用本文: 江一苇, 顾幸生. 基于网格加速与顺序选取策略的图像匹配算法[J]. 华东理工大学学报(自然科学版), 2022, 48(5): 657-664. DOI: 10.14135/j.cnki.1006-3080.20210401002
    JIANG Yiwei, GU Xingsheng. Image Matching Algorithm Based on Grid Acceleration and Sequential Selection Strategy[J]. Journal of East China University of Science and Technology, 2022, 48(5): 657-664. DOI: 10.14135/j.cnki.1006-3080.20210401002
    Citation: JIANG Yiwei, GU Xingsheng. Image Matching Algorithm Based on Grid Acceleration and Sequential Selection Strategy[J]. Journal of East China University of Science and Technology, 2022, 48(5): 657-664. DOI: 10.14135/j.cnki.1006-3080.20210401002

    基于网格加速与顺序选取策略的图像匹配算法

    Image Matching Algorithm Based on Grid Acceleration and Sequential Selection Strategy

    • 摘要: 特征点匹配可以在输入的图像之间生成一个对应关系,它是视觉里程计中一个基础且重要的模块,并且在不同的计算机视觉领域中有着广泛的应用。随机抽样一致(Random Sample Consensus, RANSAC)算法是一种应用较广的图像匹配算法,但存在召回率较低且耗时较长的问题。本文基于网格运动统计方法与顺序选取策略,提出了RANSAC改进算法。首先,对初始特征匹配进行质量排序,并在此基础上将输入图像划分为一定数量的网格,根据运动平滑性理论进行网格内匹配统计;然后,选取评分高的网格分别进行局部单应性矩阵估算;最终,将局部单应性矩阵进行聚合,进一步消除噪声影响,得到最优模型。此外,求取单应性矩阵时采用了顺序选取策略,进一步提升了算法的效率。仿真结果表明,本文方法具有较明显的优越性。

       

      Abstract: The feature point matching can generate a corresponding relationship between the input images. It is a basic and important module in visual odometry and has a wide application in different computer vision fields. Random sample consensus (RANSAC) is a widely used image matching algorithm, but it has the disadvantages of low recall rate and long time-consuming. By considering the grid motion statistics method and the sequence selection strategy, this paper proposes an improved RANSAC algorithm. Firstly, the quality of the initial feature matching is sorted, based which the input image is divided into a certain number of grids and the matching statistics in the grid is performed according to the motion smoothness theory. Then the grids with higher scores are selected to estimate the local homography matrix, respectively. Moreover, the local homography matrices are aggregated to further eliminate the influence of noise and obtain the optimal model. In addition, the sequential selection strategy is used to obtain the homography matrix, which further improves the efficiency of the proposed algorithm. Finally, the simulation results show that the proposed image matching algorithm based on grid acceleration and sequential selection strategy has better performance.

       

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