基于Laplacian的局部特征描述算法

唐永鹤; 卢焕章; 胡谋法

doi:10.3788/OPE.20111912.2999

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基于Laplacian的局部特征描述算法

信息科学 | 更新时间：2020-08-12

- 基于Laplacian的局部特征描述算法
- Local feature description algorithm based on Laplacian
- 光学精密工程 2011年19卷第12期页码：2999-3006
- 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.20111912.2999
  中图分类号： TP391.4
- 收稿日期：2011-03-17，
  
  修回日期：2011-04-15，
  
  网络出版日期：2011-12-25，
  
  纸质出版日期：2011-12-25
- 稿件说明：
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唐永鹤, 卢焕章, 胡谋法. 基于Laplacian的局部特征描述算法[J]. 光学精密工程, 2011,19(12): 2999-3006

TANG Yong-he, LU Huan-zhang, HU Mou-fa. Local feature description algorithm based on Laplacian[J]. Editorial Office of Optics and Precision Engineering, 2011,19(12): 2999-3006
唐永鹤, 卢焕章, 胡谋法. 基于Laplacian的局部特征描述算法[J]. 光学精密工程, 2011,19(12): 2999-3006 DOI： 10.3788/OPE.20111912.2999.

TANG Yong-he, LU Huan-zhang, HU Mou-fa. Local feature description algorithm based on Laplacian[J]. Editorial Office of Optics and Precision Engineering, 2011,19(12): 2999-3006 DOI： 10.3788/OPE.20111912.2999.

摘要

为了更好地兼顾特征描述子的鲁棒性和生成复杂度

提出了一种基于Laplacian的局部特征描述算法。分析说明了Laplacian不仅对图像的欧氏变换、缩放及亮度线性变化具有较好的性质

而且能够反映图像的局部结构特征。据此利用高斯型拉普拉斯变换响应建立了一种64维特征描述子

并将该特征描述子应用于特征点匹配。匹配实验结果表明

在图像尺度缩放、旋转、模糊、亮度变化和较小视角变化等多种变换条件下

该描述子不仅能够取得较好的匹配效果

而且匹配速度是尺度不变特征变换(SIFT)的4倍以上。该算法适用于实时性要求较高

存在旋转、尺度缩放、亮度差异、图像压缩变换以及视角变化不大的结构图像间的匹配。

Abstract

In order to balance the robustness and building complexity of a feature descriptor

a local feature description algorithm based on Laplacian is presented. It is analyzed and illustrated that the Laplacian not only has good properties to Euclidian transformation

zoom

and linear brightness changes of an image

but also can characterize the local structure of the image. On the basis of that

a 64-dimension descriptor is built with the response of Laplacian of Gaussian. Finally

the descriptor is used to match feature points with the absolute distance as similarity measurement. Simulation results indicate that the proposed descriptor can obtain better matching results for the image with zoom

rotation

blurring

illumination varying as well as smaller viewpoint changes

and the matching speed is more than 4 times that of Scale Invariable Feature Transformation(SIFT). The proposed feature description algorithm is suitable for matching the images of structured scenes

for it is insensitive to the image transformation with rotation

zoom

luminance varying

compression or small viewpoint changes.

关键词

Keywords

references

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