基于灰度差分不变量的快速局部特征描述算法

唐永鹤; 卢焕章

doi:10.3788/OPE.20122002.0447

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基于灰度差分不变量的快速局部特征描述算法

信息科学 | 更新时间：2020-08-12

- 基于灰度差分不变量的快速局部特征描述算法
- Fast local feature description algorithm based on greyvalue differential invariants
- 光学精密工程 2012年20卷第2期页码：447-454
- 作者机构：
  
  国防科学技术大学 ATR实验室,湖南长沙 410073
- 作者简介：
- 基金信息：
  
  国家863高技术研究发展计划资助项目(No.2015AA123703)
- DOI：10.3788/OPE.20122002.0447
  中图分类号： TP391
- 收稿日期：2011-10-11，
  
  修回日期：2011-10-28，
  
  网络出版日期：2012-02-25，
  
  纸质出版日期：2012-02-25
- 稿件说明：
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唐永鹤, 卢焕章. 基于灰度差分不变量的快速局部特征描述算法[J]. 光学精密工程, 2012,20(2): 447-454

TANG Yong-he, LU Huan-zhang. Fast local feature description algorithm based on greyvalue differential invariants[J]. Editorial Office of Optics and Precision Engineering, 2012,20(2): 447-454
唐永鹤, 卢焕章. 基于灰度差分不变量的快速局部特征描述算法[J]. 光学精密工程, 2012,20(2): 447-454 DOI： 10.3788/OPE.20122002.0447.

TANG Yong-he, LU Huan-zhang. Fast local feature description algorithm based on greyvalue differential invariants[J]. Editorial Office of Optics and Precision Engineering, 2012,20(2): 447-454 DOI： 10.3788/OPE.20122002.0447.

摘要

提出了一种基于灰度差分不变量区域统计直方图的快速局部特征描述算法来解决传统灰度差分不变量特征描述子计算复杂、稳定性较差且包含的信息量较少的问题。采用低阶且具有微分几何意义的灰度差分不变量描述特征点以降低特征描述子的计算复杂度

提高特征描述子的稳定性;利用特征点邻域的灰度信息和区域信息提高特征描述子的信息含量

增强特征描述子的鲁棒性。将该算法应用于图像匹配。实验结果表明

在图像尺度缩放、旋转、模糊、亮度变化、较小视角变化和JPEG压缩等多种变换条件下

该描述子不仅能够取得较好的匹配效果

而且处理速度比尺度不换特征变换(SIFT)提高约2倍。

Abstract

A fast local feature description algorithm based on the region histogram of Greyvalue Differential Invariants(GDIs) is presented to improve the traditional GDI feature descriptor with complex computation

worse stability and less information. The GDIs with the meaning of differential geometry derived from low order derivatives are used to describe a feature point to reduce the computation complexity of the feature descriptor and enhance the stability. The intensity and relative location of the feature point neighborhood are made full use of to increase the content of information and improve the robustness of the feature descriptor. Finally

the feature description is used to match images.The experiment results demonstrate that the proposed algorithm can get better matching results in the cases of image zoom

rotation

blurring

illumination varying

smaller viewpoint changes as well as JPEG compression. Furthermore

the processing speed is about twice that of the Scale Invariable Feature Transform(SIFT).

关键词

Keywords

references

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