基于字典描述向量的实时图像配准

王健博; 朱明

doi:10.3788/OPE.20142206.1613

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基于字典描述向量的实时图像配准

信息科学 | 更新时间：2020-08-13

- 基于字典描述向量的实时图像配准
- Real time image registration based on dictionary feature descriptor
- 光学精密工程 2014年22卷第6期页码：1613-1621
- 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. 中国科学院长春光学精密机械与物理研究所中国科学院航空光学成像与测量重点实验室,吉林长春,130033
  3. 中国科学院大学北京,100039
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目（No.61172111）()
- DOI：10.3788/OPE.20142206.1613
  中图分类号： TP391.4
- 收稿日期：2013-04-07，
  
  修回日期：2013-05-09，
  
  纸质出版日期：2014-06-25
- 稿件说明：
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王健博, 朱明,. 基于字典描述向量的实时图像配准[J]. 光学精密工程, 2014,22(6): 1613-1621

WANG Jian-bo, ZHU Ming,. Real time image registration based on dictionary feature descriptor[J]. Editorial Office of Optics and Precision Engineering, 2014,22(6): 1613-1621
王健博, 朱明,. 基于字典描述向量的实时图像配准[J]. 光学精密工程, 2014,22(6): 1613-1621 DOI： 10.3788/OPE.20142206.1613.

WANG Jian-bo, ZHU Ming,. Real time image registration based on dictionary feature descriptor[J]. Editorial Office of Optics and Precision Engineering, 2014,22(6): 1613-1621 DOI： 10.3788/OPE.20142206.1613.

摘要

针对传统的特征向量计算方法复杂度高、耗时长、占用内存多等缺点，提出了一种基于字典描述向量的图像配准方法。该算法采用K-奇异值分解（K-SVD）方法生成字典，通过比较特征点临近区域图像与字典中基底图像的相似性得到特征描述向量，从而降低了描述向量的计算复杂度，提高了算法的实时性。实施该算法时，首先通过随机KD树算法对参考图像和待配准图像的特征点进行匹配，然后使用经典随机抽样一致性（RANSAC）算法剔除误匹配点对，最后应用最小二乘法对得到的匹配点对进行参数估计，从而得到两幅待配准图像的空间几何变换关系。实验表明结果，本文提出的描述向量计算方法降低了描述向量的存储空间，加快了特征匹配的速度，可在保证配准准确度的前提下实现配准过程。

Abstract

As traditional description vector calculation method used in image registration is too complex

time consuming and taking up more memory

a novel dictionary based local feature description algorithm was proposed. The K-singular Value Decomposition( KSVD ) method was used to generate dictionary and the feature descriptor was obtained by comparing the similarity between feature point region in images and elements in the dictionary. By above

the description vector generation algorithm was simplified and a higher feature matching speed was obtained. The matching process could be carried out by using randomized KD(

-dimension)tree algorithm. Then

the Random Sample Consensus (RANSAC) was used to choose the correct matching pairs. Finally

the transform parameters were estimated by using the least square method and the space geometric transformation of two images to be registrated was obtained. Results from experiments show that the proposed method reduces the description vector storage space

speeds up the feature matching and implements the registration process in real time.

关键词

Keywords

references

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 Chinaese)

SONG Z L, LI S, GEORGE T F. Remote sensing image registration approach based on a retrofitted SIFT algorithm and Lissajous-curve trajectories [J]. Optics Express, 2010, 18(2): 513-522.

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. SUN H, MA T W. Sub-pixel motion estimation based on phase-only correlation [J].Chinese Journal of Liquid Crystals and Displays, 2011,26(6):858-862. (in Chinaese)

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,2012,27(6):827-831. QIU W T,ZHAO J,LIU J.Image matching algorithm combing SIFT with region segment [J]. Chinese Journal of Liquid Crystals and Displays,2012,27(6):827-831. (in Chinaese)

WONG A. An adaptive monte Carlo approach to phase-based multimodal image registration[J]. IEEE Transactions on Information Technology in Biomedicine, 2010, 14(1):173-179.

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

祁燕,王琰,王明宇. 改进的SIFT特征图像配准算法 [J]. 沈阳理工大学学报, 2012,31(4):50-53. QI Y,WANG Y,WANG M Y. Improved SIFT feature image registration algorithm [J]. Journal of Shenyang Ligong University,2012,31(4):50-53.

BAY H, TUYTELLARS T, GOOL L V. SURF: speeded up robust features[J]. Computer Vision and Image Understanding, 2008, 110(3):346-359.

张锐娟,张建奇,杨翠. 基于SURF的图像配准方法研究[J]. 红外与激光工程,2009, 38(1):160-165. ZHANG R J,ZHANG J Q,YANG C. Image registration approach based on SURF [J]. Infrared and Laser Engineering, 2009,38(1):160-165.(in Chinese)

CANDES E J, ROMBERG J, TAO T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2):489- 509.

AHARON M,ELAD M,BRUCKSTEIN A. K-SVD;An algorithm for designing overcomplete dictionaries for sparse representation [J]. IEEE Transactions on Signal Processing, 2006, 54(11):4311- 4322.

JOLLOFFE I T. Principal Component Analysis [M]. New York: Springer,2002.

ENGAN K,AASE S O,HUSOY J H.Multi-frame compression; Theroy and Design [J]. Eurasip Signal Processing,2000,80(10):2121-2140.

SILPA-ANAN C, HARTLEY R. Optimised KD-trees for fast image descriptor matching [C]. Computer Vision and Pattern Recognition, 2008: 1-8.

MUJA M, LOWE D G. Fast approximate nearest neighbors with automatic algorithm configuration [C]. International Conference on Computer Vision Theory and Applications, 2009:331-340.

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