SIFT matching with color invariant characteristics and global context

WANG Rui; ZHU Zheng-dan

doi:10.3788/OPE.20152301.0295

您当前的位置：

首页 >

文章列表页 >

SIFT matching with color invariant characteristics and global context

更新时间：2020-08-13

- SIFT matching with color invariant characteristics and global context
- Optics and Precision Engineering Vol. 23, Issue 1, Pages: 295-301(2015)
- 作者机构：
  
  北京航空航天大学仪器科学与光电工程学院北京,100191
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152301.0295
  CLC： TP751.1
- Received：29 October 2014，
  
  Revised：07 November 2014，
  
  Published：25 January 2015
- 稿件说明：
移动端阅览
王睿, 朱正丹,. 融合全局-颜色信息的尺度不变特征变换[J]. 光学精密工程, 2015,23(1): 295-301

WANG Rui, ZHU Zheng-dan,. SIFT matching with color invariant characteristics and global context[J]. Editorial Office of Optics and Precision Engineering, 2015,23(1): 295-301
王睿, 朱正丹,. 融合全局-颜色信息的尺度不变特征变换[J]. 光学精密工程, 2015,23(1): 295-301 DOI： 10.3788/OPE.20152301.0295.

WANG Rui, ZHU Zheng-dan,. SIFT matching with color invariant characteristics and global context[J]. Editorial Office of Optics and Precision Engineering, 2015,23(1): 295-301 DOI： 10.3788/OPE.20152301.0295.

摘要

由于尺度不变特征变换(SIFT)算法只针对图像的局部特征进行描述且忽略了图像的彩色信息

当待匹配图像中存在大量形状相似区域时

误匹配率很高. 本文对SIFT图像匹配法进行了改进

提出了SCARF(Shape-color Alliance Robust Feature)图像匹配算法.为解决SIFT常出现的误匹配现象

构造的SCARF算子利用SIFT检测子提取图像的特征点集

通过建立同心圆坐标系

在SIFT原有框架的基础上融入全局形状信息和颜色不变信息

并采用欧氏距离作为匹配代价函数进行描述子匹配.对包括SCARF算法和SIFT算法在内的5种不同匹配算法通过INRIA数据库进行了实验验证

实验结果表明:SCARF算法在图像模糊、局部特征相似、JEPG压缩和光照变化等复杂变换情况下

匹配准确率优于SIFT等其他算法

降低了误匹配的概率

明显提高了匹配的稳定性和鲁棒性.

Abstract

As Scale Invariant Feature Transform(SIFT)describes local characteristics of images only and ignores the color information of the images

it has higher match errors when a lot of similar regions in the images are matched. This paper improves the SIFT algorithm and proposes a novel method as an extension of the SIFT

called a Shape-color Alliance Robust Feature (SCARF) descriptor

to resolve the problems mentioned above. The proposed approach SCARF uses the SIFT descriptor to extract the feature point set of the images. Then

by building a concentric-ring model

it integrates a color invariant space and a shape context with the SIFT to construct the SCARF descriptor

and uses the Euclidean distance as cost function to match the descriptor. A comparative evaluation for different descriptors is carried out by the INRIA database

which verifies that the SCARF approach provides better results than other four state-of-the-art related methods in many cases

such as viewpoint change

zoom+ rotation

image blur and illumination change. It concludes that the SCARF reduces the probability of mismatch and improves the stability and robustness of matching process greatly.

关键词

Keywords

references

刘志文, 刘定生, 刘鹏. 应用尺度不变特征变换的多源遥感影像特征点匹配[J]. 光学精密工程, 2013, 21(8): 2146-2153. LIU ZH W, LIU D SH, LIU P. SIFT feature matching algorithm of multi-source remote image [J]. Opt.Precision Eng., 2013,21(8):2146-2153. (in Chinese)

赵立荣, 朱玮, 曹永刚, 等. 改进的加速鲁棒特征算法在特征匹配中的应用[J]. 光学精密工程, 2013, 21(12): 3263-3271. ZHAO L R, ZHU W, CAO Y G, et al.. Application of improved SURF algorithm to feature matching [J]. Opt.Precision Eng., 2013,21(12): 3263-3271. (in Chinese)

韩冬松, 何昕, 魏仲慧, 等. 采用区域特征匹配的三维弹痕自动配准[J]. 液晶与显示, 2014, 29(5): 761-767. HAN D S, HE X, WEI ZH H, et al.. Automatic registration of 3-D bullet marks by matching regional features [J]. Chinese Journal of Liquid Crystals and Displays,2014,29(5):761-767.(in Chinese)

LOWE D G. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

MIKOLAJCZYK K, SCHMID C. A performance evaluation of local descriptors [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1615-1630.

MORTENSEN E N, DENG H, SHAPIRO L. A SIFT descriptor with global context [C].2005 IEEE Computer Society Conference on CVPR, 2005: 184-190.

BAY H, TUYTELAARS T, VAN GOOL L. Surf: Speeded up Robust Features [M]. Computer Vision-ECCV 2006, Springer, 2006: 404-417.

苏可心, 韩广良, 孙海江. 基于SURF的抗视角变换图像匹配算法[J]. 液晶与显示, 2013, 28(4): 626-632. SU K X, HAN G L, SUN H J. Anti-view point changing image matching algorithm based on SURF[J]. Chinese journal of Liquid Crystals and Displays,2013,28(4):626-632. (in Chinese)

ABDEL-HAKIM A E, FARAG A A. CSIFT: A SIFT descriptor with color invariant characteristics [C].2006 IEEE Computer Society Conference on Proceedings of the Computer Vision and Pattern Recognition, IEEE, 2006: 1978-1983.

纪华, 吴元昊, 孙宏海,等. 结合全局信息的SIFT特征匹配算法 [J]. 光学精密工程, 2009, 17(2): 439-444. JI H, WU Y H, SUN H H,et al.. SIFT feature matching algorithm with global information [J]. Opt.Precision Eng., 2009, 17(2): 439-444. (in Chinese)

曾峦, 王元钦, 谭久彬. 改进的SIFT特征提取和匹配算法 [J]. 光学精密工程, 2011, 19(6): 1391-1397. ZENG L, WANG Y Q, TAN J B, Improved algorithm for SIFT feature extraction and matching [J]. Opt.Precision Eng., 2011, 19(6):1391-1397.(in Chinese)

王灿进, 孙涛, 陈娟. 局部不变特征匹配的并行加速技术研究 [J]. 液晶与显示, 2014, 29(2): 266-274. WANG C J, SUN T, CHEN J. Speeding up local invariant feature matching using parallel technology [J]. Chinese Journal of Liquid Crystals and Displays, 2014, 29(2): 266-274.(in Chinese)

Views

402

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Fast object recognition under multiple varying background using improved SIFT method

SAR image multi-scale registration based on FKICA-SIFT features

Related Author

NIE Hai-tao

LONG Ke-hui

MA Jun

LIU Jin-guo

LIU Xiang-zeng

TIAN Zheng

SHI Zhen-guang

CHEN Zhan-shou

Related Institution

Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China

University of Chinese Academy of Sciences, Beijing 100039, China

西北工业大学理学院

中国科学院长春光学精密机械与物理研究所

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰