Image Matching Algorithm Combining SIFT with Region Segmentation

QIU Wen-tao; ZHAO Jian; LIU Jie

doi:10.3788/YJYXS20122706.0827

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Image Matching Algorithm Combining SIFT with Region Segmentation

Imaging Technology & Image Processing | 更新时间：2020-08-12

- Image Matching Algorithm Combining SIFT with Region Segmentation
- Chinese Journal of Liquid Crystals and Displays Vol. 27, Issue 6, Pages: 827-831(2012)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院研究生院北京,100049
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20122706.0827
  CLC： TP391.9
- Received：23 March 2012，
  
  Revised：11 June 2012，
  
  Published：15 December 2012
- 稿件说明：
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丘文涛, 赵建, 刘杰. 结合区域分割的SIFT图像匹配方法[J]. 液晶与显示, 2012,(6): 827-831

QIU Wen-tao, ZHAO Jian, LIU Jie. Image Matching Algorithm Combining SIFT with Region Segmentation[J]. , 2012,(6): 827-831
丘文涛, 赵建, 刘杰. 结合区域分割的SIFT图像匹配方法[J]. 液晶与显示, 2012,(6): 827-831 DOI： 10.3788/YJYXS20122706.0827.

QIU Wen-tao, ZHAO Jian, LIU Jie. Image Matching Algorithm Combining SIFT with Region Segmentation[J]. , 2012,(6): 827-831 DOI： 10.3788/YJYXS20122706.0827.

摘要

针对待配准图在参考图上存在多个相似的区域

传统的SIFT算法导致匹配点数量较少

影响对模型变换参数估计的情况

提出了结合区域分割的SIFT方法。与原始算法相比

该算法可以得到更多正确的匹配点对

时效性上更优。实验结果表明

该算法比原算法的正确匹配点对提高了近30倍

结合区域分割的特征匹配

剔除了90%以上的误匹配点对

改进后的算法时间性能上也更优。

Abstract

Aiming at solving the situation that the original SIFT algorithm can only get few number of matching points if there are a few regions similar to the matching image on the reference image

which will affect the estimation of parameters in the transformation model

this paper proposed a method combining SIFT with region segmentation. Compared to the original method

our method can obtain much more correct matching points

and is less time consuming. The experiments demonstrate that our method can acquire nearly 30 times more correct matching points than the original one. Combining region segmentation with the original SIFT feature matching

this method can eliminate at least 90% of the erroneous matching points

besides the improved algorithm can lower the computational burden.

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references

王永明,王贵锦. 图像局部不变性特征与描述 [M].北京:国防工业出版社,2010: 79-87,129-133,150-163.[2] 倪国强, 刘琼. 多源图像配准技术分析与展望 [J]. 光电工程, 2004, 31(1):1-6.[3] Tuyte T, Mikolajczyk K. Local Invariant Feature Detectors: A Survey. Foundations and Trends in Computer Graphics and Vision [M]. Delft, Netherlands:Now Publishers Inc., 2008.[4] Lowe D G. Distinctive image features from scale-invariant keypoints [J].International Journal of Computer vision, 2004, 60(2), 91-110.[5] Ke Y, Sukthankar R. PCA SIFT: a more distinctive representation for local image descriptors [C]//Proc. Conf. Computer Vision and Pattern Recognition, Waskington, USA:IEEE, 2004: 511-517.[6] Mikolajczyk K, Schmid C. A performance evaluation of local descriptors [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,(27): 1615-1630.[7] Herbert B, Tinne T, Luc V G. SURF: speeded up robust features [J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359.[8] 李英, 李静宇, 徐正平. 结合SURF与聚类分析方法实现运动目标的快速跟踪 [J]. 液晶与显示, 2011, 26(4):544-550.[9] 孙辉,马天玮. 基于相位相关的目标图像亚像元运动参数估计 [J]. 液晶与显示, 2011,26(6):858-862.[10] Koenderink J. The structure of image [J]. Biological Cybernetics, 1984, 50:363-396.[11] Lindeberg T. Scale-space for discrete signals [J]. IEEE Transaction PAMI, 1980, 207:187-207.[12] 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.[13] 邸男,李桂菊,魏雅娟. 采用SIFT的末制导图像匹配技术 [J]. 红外与激光工程, 2011,40(8):1549-1593.

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