国防科技大学学报

引用本文:	戴卫华,刘盛春,赵慎,等.采用局域像素匹配的随机抽样一致改进算法.[J].国防科技大学学报,2021,43(4):38-43.[点击复制]
	DAI Weihua,LIU Shengchun,ZHAO Shen,et al.Improved random sampling consensus algorithm using local pixel matching[J].Journal of National University of Defense Technology,2021,43(4):38-43[点击复制]

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采用局域像素匹配的随机抽样一致改进算法

戴卫华¹,刘盛春²,赵慎³ ,彭华¹,张昊¹,黄志刚⁴,李小林⁵

(1. 信息工程大学信息系统工程学院, 河南郑州 450001;2. 哈尔滨工程大学水声工程学院, 黑龙江哈尔滨 150006;3. 陆军工程大学石家庄校区, 河北石家庄 050003;4. 盲信号处理国防科技重点实验室, 四川成都 610041;5. 拉盖尔电子科技有限公司, 湖南长沙 410073)

摘要:

为提高图像拼接的配准精度和稳健性,提出基于局域像素匹配的随机抽样一致改进算法。在完成尺度不变特征变换算子或其他算子图像特征提取、特征匹配之后,利用独立于特征匹配点的局域像素,通过参考图像局域像素与映射的待拼接图像局域像素匹配,优选4对最佳特征匹配点,确定最佳单应矩阵。实验结果表明:与随机抽样一致经典算法相比,该方法未明显增加计算耗时,单应矩阵更准确,图像拼接稳健性更好。

关键词: 图像拼接单应矩阵随机抽样一致算法像素匹配

DOI：10.11887/j.cn.202104006

投稿日期：2019-11-20

基金项目:国家自然科学基金资助项目(61802430)

Improved random sampling consensus algorithm using local pixel matching

DAI Weihua¹, LIU Shengchun², ZHAO Shen³, PENG Hua¹, ZHANG Hao¹, HUANG Zhigang⁴, LI Xiaolin⁵

(1. College of Information Systems Engineering, Information Engineering University, Zhengzhou 450001, China;2. College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150006, China;3. Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China;4. National Key Laboratory on Blind Signal Processing, Chengdu 610041, China;5. Laguerre Electronic Technology Company, Changsha 410073, China)

Abstract:

In order to improve the registration precision and robustness of image splicing, an improved random sampling consensus algorithm based on local pixel matching was proposed. After completing image feature extracting and feature matching with the scale invariant feature transform operator or other operator, using the local pixels that are independent of feature matching points, then the optimal configuration of the four pairs of feature matching points, and the best homography matrix were determined by matching the local pixel of the reference image with the mapped local pixel of the image to be stitched. The experimental results show that, compared with the classical random sampling consensus algorithm, the computation time is close, the homography matrix is more accurate, and the image mosaic is more robust.

Keywords: image splicing homography matrix random sampling consensus algorithm pixel matching