Fast local feature description algorithm based on greyvalue differential invariants

TANG Yong-he; LU Huan-zhang

doi:10.3788/OPE.20122002.0447

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Fast local feature description algorithm based on greyvalue differential invariants

Article | 更新时间：2020-08-12

- Fast local feature description algorithm based on greyvalue differential invariants
- Optics and Precision Engineering Vol. 20, Issue 2, Pages: 447-454(2012)
- 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：
- DOI：10.3788/OPE.20122002.0447
  CLC： TP391
- Received：11 October 2011，
  
  Revised：28 October 2011，
  
  Published Online：25 February 2012，
  
  Published：25 February 2012
- 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

丁雪梅, 王维雅, 黄向东. 基于差分和特征不变量的运动目标检测与跟踪[J]. 光学精密工程,2007, 15(4):570-576. DING X M, WANG W Y, HUANG X D. New method for detecting and tracking of moving target based on difference and invariant[J]. Opt. Precision Eng., 2007, 15(4):570-576. (in Chinese).[2] 王会峰, 刘上乾,汪大宝,等. 基于序列图像特征配准的摄像机补偿算法[J]. 光学精密工程, 2008, 16(7):1330-1334. WANG H F, LIU SH Q, WANG D B, et al.. Video camera rotation compensation algorithm based on feature matching of sequences image frames[J]. Opt. Precision Eng., 2008,16(7):1330-1334. (in Chinese)[3] LEE L, KO H. Gradient-based local affine invariant feature extraction for mobile robot localization in indoor environments[J]. Pattern Recognition Letters, 2008, 29(14):1934-1940.[4] 杨晓敏, 吴炜, 卿粼波,等. 图像特征点提取及匹配技术[J]. 光学精密工程, 2009, 17(9):2276-2282. YANG X M, WU W, QING L B, et al.. Image feature extraction and matching technology[J]. Opt. Precision Eng., 2009, 17(9):2276-2282. (in Chinese).[5] YANG D D, ANDRZEJ S. A low-dimensional local descriptor incorporating TPS warping for image matching[J]. Image and Vision Computing, 2010, 28:1184-1195.[6] DONGJOE S, TARDI T. Clique descriptor of affine invariant regions for robust wide baseline image matching[J]. Pattern Recognition, 2010,43:3261-3272.[7] LORIS N, ALESSANDRA L, SHERYL B. Local binary patterns variants as texture descriptors for medical image analysis[J]. Artificial Intelligence in Medicine, 2010, 49:117-125.[8] LOWE D G. Distinctive image features from scale invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2):91-110.[9] MIKOLAJCZYK K, SCHMID C. A performance evaluation of local descriptors[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005,27(10):1615-1630.[10] BAY H, TUYTELAARS T, GOOL L V. SURF:Speeded up robust features . Proceedings of the 9th European Conference on Computer Vision, Graz, Austria, 2006:404-417.[11] WU F C,WANG Z H, WANG X G. Feature vector field and feature matching[J]. Pattern Recognition, 2010,43:3273-3281. [12] 王旭光, 王志衡, 吴福朝. Harris相关与特征匹配[J]. 模式识别与人工智能, 2009, 22(4):505-513. WANG X G, WANG Z H, WU F C. Harris correlation and feature matching[J]. PR &AI, 2009, 22(4): 505-513. (in Chinese)[13] SCHMI C, MOHR R. Matching by local invariants .Technical Report INRIA, France, 1995.

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