电子测量与仪器学报

冉宁,范晨锋,张少康,邵占青,郝晋渊.一种改进ORB特征点提取与匹配的图像处理算法[J].电子测量与仪器学报,2025,39(4):213-224

一种改进ORB特征点提取与匹配的图像处理算法

Research on image processing based on improve ORBfeature extraction and matching

DOI：

英文关键词:feature extraction feature matching GMS dynamic Bayesian network

基金项目:国家自然科学基金（62373132）、中央引导地方科技发展资金项目（236Z1602G）、石家庄市驻冀高校基础研究项目（241791367A）、保定市科技计划项目（2472P006）、河北大学优秀青年科研创新团队建设项目（QNTD202411）、河北大学多学科交叉研究计划项目（DXK202409）资助

作者	单位
冉宁	1.河北大学电子信息工程学院保定071002；2.河北大学节能技术研发中心保定071002
范晨锋	河北大学电子信息工程学院保定071002
张少康	河北大学网络空间安全与计算机学院保定071002
邵占青	保定市满城区农产品综合检测服务中心保定072150
郝晋渊	5.河北大学中央兰开夏传媒与创意学院保定071002；6. 河北大学物联网智能技术研究中心保定071002

Author	Institution
Ran Ning	1.School of Electronic Informational Engineering, Hebei University, Baoding 071002, China; 2.Laboratory of EnergySaving Technology,Hebei University, Baoding 071002, China
Fan Chenfeng	School of Electronic Informational Engineering, Hebei University, Baoding 071002, China
Zhang Shaokang	School of Cyber Security and Computer, Hebei University, Baoding 071002, China
Shao Zhanqing	Baoding City Mancheng District Agricultural Products Comprehensive Testing Service Center, Baoding 072150, China
Hao Jinyuan	5.HBU-UCLAN School of Media, Communication and Creative Industries, Hebei University, Baoding 071002, China; 6.Laboratory of IoT Technology, Hebei University, Baoding 071002, China

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中文摘要:

针对传统图像处理中快速特征检测和描述算法（ORB）特征点提取不均匀、匹配速度慢、匹配准确率不高的问题，提出了一种改进ORB特征点提取与匹配的图像处理算法。首先，使用改进的四叉树算法对ORB特征点进行均匀化提取，最大限度利用整幅图像的信息；其次，进行特征点的暴力匹配和基于网格的运动统计（GMS）筛选，对特征匹配进行初步筛选，提高匹配准确率；最后，利用动态贝叶斯网络筛选得到最佳匹配模型和最佳匹配对，提高匹配准确率的同时减少筛选时间。实验结果表明，提出的改进算法较传统算法，均匀度有明显改善，完成提取与匹配所用的平均时间较其他改进算法均有减少，匹配准确率较其他算法均有提高，特别是相比传统ORB算法提高了49.1％，整体效果明显优于传统的ORB算法及其他改进算法。证明改进ORB特征点提取与匹配的图像处理算法可以较好地实现特征匹配准确率与特征匹配速度的同步提升。

英文摘要:

To address the issues of uneven ORB feature point extraction, slow matching speed, and low matching accuracy in traditional image processing, this paper proposes an improved image processing algorithm for ORB feature extraction and matching. First, an enhanced quadtree algorithm is employed to achieve uniform extraction of ORB feature points, maximizing the utilization of information across the entire image. Second, brute-force matching combined with GMS screening is implemented to preliminarily filter feature matches, thereby enhancing matching accuracy. Finally, a dynamic Bayesian network is utilized to select the optimal matching model and best-matched pairs, further improving matching accuracy while reducing screening time. Experimental results demonstrate that compared with traditional algorithms, the proposed method significantly improves feature distribution uniformity. The average time required for feature extraction and matching is reduced compared to other improved algorithms, while achieving higher matching accuracy—specifically showing a 49.1% improvement over conventional ORB algorithms. The overall performance surpasses both traditional ORB and other existing improved algorithms. It is demonstrated that the image processing based on improve ORB feature extraction and matching can effectively achieve simultaneous improvements in both feature matching accuracy and feature matching speed.

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