变圆域罗盘特征图像匹配

李佳; 盛业华; 张卡; 段平

doi:10.3788/OPE.20142205.1339

您当前的位置：

首页 >

文章列表页 >

变圆域罗盘特征图像匹配

信息科学 | 更新时间：2020-08-13

- 变圆域罗盘特征图像匹配
- Image matching of variable circular domain compass features
- 光学精密工程 2014年22卷第5期页码：1339-1346
- 作者机构：
  
  南京师范大学虚拟地理环境教育部重点实验室,江苏南京,210023
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目（No.40901200）();国家科技支撑计划资助项目（No.2012BAH35B02）();江苏省普通高校研究生科研创新计划资助项目（No.
- DOI：10.3788/OPE.20142205.1339
  中图分类号： TP751
- 收稿日期：2013-09-16，
  
  修回日期：2013-11-10，
  
  纸质出版日期：2014-05-25
- 稿件说明：
移动端阅览
李佳, 盛业华, 张卡等. 变圆域罗盘特征图像匹配[J]. 光学精密工程, 2014,22(5): 1339-1346

LI Jia, SHENG Ye-hua, ZHANG Ka etc. Image matching of variable circular domain compass features[J]. Editorial Office of Optics and Precision Engineering, 2014,22(5): 1339-1346
李佳, 盛业华, 张卡等. 变圆域罗盘特征图像匹配[J]. 光学精密工程, 2014,22(5): 1339-1346 DOI： 10.3788/OPE.20142205.1339.

LI Jia, SHENG Ye-hua, ZHANG Ka etc. Image matching of variable circular domain compass features[J]. Editorial Office of Optics and Precision Engineering, 2014,22(5): 1339-1346 DOI： 10.3788/OPE.20142205.1339.

摘要

针对现有图像匹配算法特征算子计算复杂度高，关键点描述过程中点对选取存在不确定性等问题，提出了变圆域罗盘特征匹配（VCDCM）方法。该方法首先利用罗盘检测法对图像进行关键点检测，采用变圆模式接受域选取理想点对，根据接受域内点对之间的距离将点对分为长点对集和短点对集；然后用长点对集描述关键点方向，短点对集构建关键点描述符。最后采用Hamming距离代替传统的欧式距离进行匹配，并采用随机抽样一致（RANSAC）方法精炼匹配点以避免由于噪声和物体位置移动等原因产生的误匹配。从鲁棒性和实时性两个方面对本文提出的方法与尺度不变特征变换（SIFT）和二元加速鲁棒特征（BRIEF）方法进行了对比试验分析，实验结果表明，本文提出的方法具有匹配速度快、准确性高、稳定性好等特点。

Abstract

As existing image matching algorithms show the problems of high computational complexity and uncertainty in point-pair selection

a Variable Circular Domain Compass Matching (VCDCM) algorithm was proposed. After key-points being detected by four compasses

the variable circular receiving domain was used to choose the ideal point-pairs. Then

the point-pair set was divided into two subsets according to the distance of point-pairs.The subset of long-distance pairs was used to describe the direction of a key-point and that of short-distance pairs was used to build the descriptor of key-point. Finally

key-points were matched by Hamming distance instead of traditional Euclidean distance

while the match points were filtered with Random Sample Consensus (RANSAC) algorithm to avoid mismatches caused by the noise and moving objects. Comparative experiments between Scale Invariant Feature Transform (SIFT) and Binary Robust Independent Elementary Features (BRIEF) algorithms were performed on the robustness and efficiency. The experimental results show that the proposed algorithm is faster with high accuracy and stability.

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)

瞿优, 曾峦, 熊伟. 不同局部邻域划分加速鲁棒特征描述符的性能分析[J]. 光学精密工程, 2013, 21(9):2395-2404. ZHAI Y, ZENG L, XIONG W.Performance analysis of SURF descriptor with different local region partitions [J].Opt.Precision Eng. , 2013, 21(9):2395-2404.(in Chinese)

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

KE Y,SUKTHANKAR R.PCA-SIFT:A more distinctive representation for local image descriptors.Proceedings of the Conference on Computer Vision and Pattern Recognition, Washington, USA:IEEE, 2004:511-517.

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

BAY H, TUYTELAARS T, GOOL L V.SURF:Speeded up Robust Features.Proceedings of 9th European Conference on Computer Vision, Graz, Austria, Springer, 2006:404-417.

CALONDER M, LEPETIT V, SSTECHA C, et al.Brief:binary robust independent elementary features.Proceeding of 11th European Conference on Computer Vision, Heraklion, Crete, Greece, Springer, 2010,6314:778-792.

RUBLEE E, RABAUD V, KONOLIGE K, et al..Orb:an efficient alternative to SIFT or SURF.IEEE International Conference on Computer Vision ICCV, 2011:2564-2571.

ROSTEN E, DRUMMOND T.Machine learning for high-speed corner detection.Proceedings of the 9th European Conference on Computer Vision, Graz, Austria, 2006:430-443.

ROSTEN E, DRUMMOND T.Fusing points and lines for high performance tracking.Proceedings of the 10th IEEE International Conference on Computer Vision, Beijing, 2005:1508-1515.

ROSTEN E, DRUMMOND T.Faster and better:A Machine learning approach to corner detection [J].IEEE Transactions on Pattern Analysis and Machine Intellgence, 2010, 32(1):105-119.

LEUTENEGGER S, CHLI M, SIEGWART R.Brisk:Binary robust invariant scalable keypoints.IEEE International Conference on Computer Vision, Barcelona, 2011:2548-2555.

THI T H T, ERIC M H.Real-time keypoints matching:application to visual servoing.IEEE International Conference on Robotics and Automation, Roma, 2007:3787-3792.

FISCHLER M A, BOLLES R C.Random sample consensus:A paradigm for model fitting with applications to image analysis and automated cartography [J].Communications of the ACM, 1981, 24(6):381-395.

浏览量

260

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于CycleGAN-SIFT的可见光和红外图像匹配

具有近似仿射尺度不变特征的快速图像匹配

基于高光谱图像的改进SIFT特征提取与匹配

高强钢薄板高温焊接变形的视觉测量

用于遥感图像目标快速匹配识别的改进混合溢出树算法