基于网格加速与顺序选取策略的图像匹配算法

江一苇; 顾幸生

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

doi: 10.14135/j.cnki.1006-3080.20210401002

江一苇,
顾幸生^,

华东理工大学能源化工过程智能制造教育部重点实验室，上海 200237

基金项目: 国家自然科学基金(61973120，62076095)

详细信息

作者简介:
江一苇（1996-），男，江苏海门人，硕士生，主要研究方向为机器人技术。E-mail：jiangyiwei0105@qq.com

通讯作者:
顾幸生，E-mail：xsgu@ecust.edu.cn

中图分类号: TP242.6
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- 文章访问数: 208
- HTML全文浏览量: 108
- PDF下载量: 13
- 被引次数: 0
出版历程
- 收稿日期: 2021-04-01
- 网络出版日期: 2021-06-16

Image Matching Algorithm Based on Grid Acceleration and Sequential Selection Strategy

JIANG Yiwei,
GU Xingsheng^,

Key Laboratory of Smart Manufacturing in Energy Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

摘要

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。随机抽样一致算法(Random Sample Consensus, RANSAC)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。
- 特征点匹配 /
- 随机抽样一致算法 /
- 网格统计 /
- 顺序选取策略
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.
- feature point matching /
- RANSAC /
- grid statistics /
- sequential selection strategy

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图 1 运动统计示意图

Figure 1. Demonstration of motion statistics

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图 2 网格框架

Figure 2. Grid-based framework

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图 3 8邻域算子模板

Figure 3. Eight-neighborhood Laplace operator

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图 4 求解高分网格的单应性矩阵

Figure 4. Solve the homography matrix of a high-resolution grid

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图 5 模型聚合示意图

Figure 5. Demonstration of model aggregation

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图 6 顺序选取示意图

Figure 6. Schematic diagram of sequential selection

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图 7 数据集示意图

Figure 7. Demonstration of dataset

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图 8 不同模糊程度图像结果对比图

Figure 8. Comparison of blur variation

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图 9 不同亮度图像结果对比图

Figure 9. Comparison of luminance variation

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图 10 旋转+缩放图像结果对比图

Figure 10. Comparison of scaling and rotation

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表 1 SIFT特征点仿真实验结果

Table 1. Simulation experiment results of SIFT

Image group	Algorithm	Average precision	Average recall
Luminance variation	RANSAC	0.813	0.782
	RANSAC+GMS	0.863	0.824
	Proposed algorithm	0.899	0.836
Scaling and rotation	RANSAC	0.792	0.712
	RANSAC+GMS	0.870	0.768
	Proposed algorithm	0.902	0.818
Blur variation	RANSAC	0.804	0.785
	RANSAC+GMS	0.822	0.803
	Proposed algorithm	0.869	0.846

下载: 导出CSV

表 2 算法耗时对比

Table 2. Comparison of algorithm time consumption

Number of feature points	Time consuming/ms
Number of feature points	RANSAC	GMS+RANSAC	Proposed algorithm
1000	80.70	53.34	58.78
2000	115.80	70.80	78.23
5000	240.10	123.16	135.89
10000	553.50	296.90	304.50

下载: 导出CSV

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