A panoramic image registration algorithm based on SURF

LIANG Yan-ju1; LI Qing1; LIN Zhen-zhen1; CHEN Da-peng2

doi:10.13873/j.1000-97872012.05.029

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A panoramic image registration algorithm based on SURF

更新时间：2025-09-11

- A panoramic image registration algorithm based on SURF
- Vol. 31, Issue 5, Pages: 132-135(2012)
- 作者机构：
  
  1. 中国科学院微电子所昆山感知中心
  2. 中国科学院微电子研究所集成先导工艺研究中心
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872012.05.029
  CLC： TP391.41
- Published：2012
- 稿件说明：
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[1]梁艳菊,李庆,林蓁蓁,陈大鹏.一种基于SURF的全景图像配准算法[J].传感器与微系统,2012,31(05):132-135.

LIANG Yan-ju1, LI Qing1, LIN Zhen-zhen1, et al. A panoramic image registration algorithm based on SURF[J]. 2012, 31(5): 132-135.
[1]梁艳菊,李庆,林蓁蓁,陈大鹏.一种基于SURF的全景图像配准算法[J].传感器与微系统,2012,31(05):132-135. DOI： 10.13873/j.1000-97872012.05.029.

LIANG Yan-ju1, LI Qing1, LIN Zhen-zhen1, et al. A panoramic image registration algorithm based on SURF[J]. 2012, 31(5): 132-135. DOI： 10.13873/j.1000-97872012.05.029.

摘要

传统的全景图像配准多采用基于SIFT的方法

该方法数据量大、时间效率低。提出了一种基于SURF的全景图像快速配准方法。运用SURF提取特征点

计算特征描述符;运用低时间复杂度的K-D树最近邻搜索法实现特征点快速匹配;利用RANSAC算法剔除误匹配点;最后估计出两幅全景图像的变换矩阵。测试表明:算法具有较高的时间效率和良好的鲁棒性。

Abstract

Previous panoramic images registration use approaches based on SIFT which has huge data amount and low time-efficiency.Technique for panoramic image registration is presented

SURF（speeded up robust features）is used to detect and descript the interest points

match the interest points by using high time-efficient K-D tree nearest neighbor searching method.Mismatched points is eliminated utilizing RANSAC.Transformation matrix between images is estimated.The experimental result shows that it has good time efficiency and excellent robustness.

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references

改进的RANSAC算法在图像配准中的应用 [J]. 曲天伟,安波,陈桂兰. 计算机应用 . 2010(07)

基于SURF的图像配准方法研究 [J]. 张锐娟,张建奇,杨翠. 红外与激光工程 . 2009(01)

一种基于广义互信息的图像配准算法 [J]. 张珍,曹云峰,丁萌,冯成涛. 传感器与微系统 . 2008(04)

基于kd树的多维索引在数据库中的运用 [J]. 吴涵,杨克俭. 自动化技术与应用 . 2007(09)

Scale & Affine Invariant Interest Point Detectors [J] . Krystian Mikolajczyk,Cordelia Schmid. International Journal of Computer Vision . 2004 (1)

Distinctive Image Features from Scale-Invariant Keypoints [J] . David G. Lowe. International Journal of Computer Vision . 2004 (2)

SUSAN—A New Approach to Low Level Image Processing [J] . Stephen M. Smith,J. Michael Brady. International Journal of Computer Vision . 1997 (1)

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