WU Wei, SHEN Zhanfeng, WANG Xianwei, WU Tianjun, WANG Weihong. An Affine Invariant-based Match Propagation Method for Quasi-dense Image Registration[J]. Geomatics and Information Science of Wuhan University, 2018, 43(6): 930-936. DOI: 10.13203/j.whugis20160146
Citation: WU Wei, SHEN Zhanfeng, WANG Xianwei, WU Tianjun, WANG Weihong. An Affine Invariant-based Match Propagation Method for Quasi-dense Image Registration[J]. Geomatics and Information Science of Wuhan University, 2018, 43(6): 930-936. DOI: 10.13203/j.whugis20160146

An Affine Invariant-based Match Propagation Method for Quasi-dense Image Registration

Funds: 

The National Natural Science Foundation of China 41301473

the Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province OBDMA201511

More Information
  • Author Bio:

    WU Wei, PhD, lecturer, specializes in remote sensing data processing and information extraction. E-mail: wuwei@zjut.edu.cn

  • Received Date: April 06, 2016
  • Published Date: June 04, 2018
  • Non-rigid models are needed to correct the non-uniform deformation between two images; dense, uniformly distributed control points is the premise of constructing such non-rigid models. This paper puts forward an affine invariant-based match propagation method for quasi-dense image registration. Interest points are extracted from the input image and reference image, respectively; and sparsely matched. According to the sparsely matched points, the global offset between the two images is estimated and corrected. A corresponding adjacent triangle is established using the interest points in the two images interactively; and an affine invariant, the ratio of the triangle area, is employed to determine whether the vertices in the two triangles match or not. Through this method, the spare initial seed matching points propagate to other points of interest to obtain quasi-dense match. The HJ-Landsat image and ETM+ data of Antarctic data are taken in an experiment, the results show that our method can extract dense and uniformly distributed matching points while outperforming other state of art methods in true positive ratio.
  • [1]
    Zitova F J. Image Registration Methods:A Survey[J]. Image and Vision Computing, 2003, 21(11):977-1000 doi: 10.1016/S0262-8856(03)00137-9
    [2]
    Mikolajczyk K, Schmid C. Scale & Affine Invariant Interest Point Detectors[J].International Journal of Computer Vision, 2004, 60(1):63-86 doi: 10.1023/B:VISI.0000027790.02288.f2
    [3]
    Lowe D G. Distinctive Image Features from Scale-Invariant Keypoints[J].International Journal of Computer Vision, 2004, 60(2):91-110 http://cn.bing.com/academic/profile?id=0ad98e73d02a03f0751d0317dd8d714c&encoded=0&v=paper_preview&mkt=zh-cn
    [4]
    Bay H, Ess A, Tuytelaars T, et al. Speeded-Up Robust Features (SURF)[J].Computer Vision and Image Understanding, 2008, 110(3):346-359 doi: 10.1016/j.cviu.2007.09.014
    [5]
    黄青青, 赵鸿志, 杨健, 等.环境星宽覆盖特征CCD影像几何定位及误差分析[J].武汉大学学报·信息科学版, 2014, 39(12):1425-1429 http://ch.whu.edu.cn/CN/abstract/abstract3135.shtml

    Huang Qingqing, Zhao Hongzhi, Yang Jian, et al. Geometric Rectification and Error Analysis for HJ-1 CCD Image with Wide Coverage Feature[J].Geomatics and Information Science of Wuhan University, 2014, 39(12):1425-1429 http://ch.whu.edu.cn/CN/abstract/abstract3135.shtml
    [6]
    Heid T, Kääb A. Evaluation of Existing Image Matching Methods for Deriving Glacier Surface Displacements Globally from Optical Satellite Imagery[J].Remote Sensing of Environment, 2012, 118:339-355 doi: 10.1016/j.rse.2011.11.024
    [7]
    Zagorchev L, Goshtasby A. A Comparative Study of Transformation Functions for Nonrigid Image Registration[J]. IEEE Transactions on Image Processing, 2006, 15(3):529-538 http://cn.bing.com/academic/profile?id=372ab2bc65a198e224bc72648c882ab4&encoded=0&v=paper_preview&mkt=zh-cn
    [8]
    Lhuillier M, Quan L. Match Propagation for Image-Based Modeling and Rendering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(8):1140-1146 http://cn.bing.com/academic/profile?id=e02749661071413b9453a1815354aa40&encoded=0&v=paper_preview&mkt=zh-cn
    [9]
    Lhuillier M, Quan L. A Quasi-Dense Approach to Surface Reconstruction from Uncalibrated Images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3):418-433 http://cn.bing.com/academic/profile?id=2a8bc4311f29717f8d64444feb4fe7d5&encoded=0&v=paper_preview&mkt=zh-cn
    [10]
    唐丽, 吴成柯, 刘侍刚, 等.基于区域增长的立体像对稠密匹配算法[J].计算机学报, 2004, 27(7):936-943 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjxb200407010

    Tang Li, Wu Chengke, Liu Shigang, et al. Image Dense Stereo Matching by Technique of Region Growing[J].Chinese Journal of Computers, 2004, 27(7):936-943 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjxb200407010
    [11]
    许振辉, 张峰, 孙凤梅, 等.基于邻域传递的鱼眼图像的准稠密匹配[J].自动化学报, 2009, 35(9):1159-1167 http://www.nlpr.ia.ac.cn/2009papers/gnkw/nk6.pdf

    Xu Zhenhui, Zhang Feng, Sun Fengmei, et al. Quasi-Dense Matching by Neighborhood Transfer for Fish-Eye Images[J].Acta Automatica Sinica, 2009, 35(9):1159-1167 http://www.nlpr.ia.ac.cn/2009papers/gnkw/nk6.pdf
    [12]
    Zhu Q, Wu B, Tian Y. Propagation Strategies for Stereo Image Matching Based on the Dynamic Triangle Constraint[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2007, 62(4):295-308 doi: 10.1016/j.isprsjprs.2007.05.010
    [13]
    杨化超, 张书毕, 张秋昭.基于SIFT的宽基线立体影像最小二乘匹配方法[J].测绘学报, 2010, 39(2):537-543 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=chxb201002014

    Yang Huachao, Zhang Shubi, Zhang Qiuzhao. Least Square Matching Methods for Wide Base-Line Stereo Images Based on SIFT Features[J].Acta Geodaetica et Cartographica Sinica, 2010, 39(2):537-543 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=chxb201002014
    [14]
    姚国标, 邓喀中, 艾海滨, 等.倾斜立体影像自动准稠密匹配与三维重建算法[J].武汉大学学报·信息科学版, 2014, 39(7):843-849 http://ch.whu.edu.cn/CN/abstract/abstract3031.shtml

    Yao Guobiao, Deng Kazhong, Ai Haibin et al. An Algorithm of Automatic Quasi-dense Matching and Three-dimensional Reconstruction for Oblique Ste-reo Image[J].Geomatics and Information Science of Wuhan University, 2014, 39(7):843-849 http://ch.whu.edu.cn/CN/abstract/abstract3031.shtml
    [15]
    吴军, 姚泽鑫, 程门门, 等.融合SIFT与SGM的倾斜航空影像密集匹配[J].遥感学报, 2015, 19(3):431-442 http://www.cnki.com.cn/Article/CJFDTotal-YGXB201503007.htm

    Wu Jun. Yao Zexin, Cheng Menmen, et al. Airborne Oblique Stereo Image Dense Matching by Integrating SIFT and SGM Algorithm[J].Journal of Remote Sensing, 2015, 19(3):431-442 http://www.cnki.com.cn/Article/CJFDTotal-YGXB201503007.htm
    [16]
    李全文, 赵卫林, 杨晓梅, 等.基于FAST测点的大幅宽HJ星图像几何精纠正方法[J].国土资源遥感, 2014, 26(4):14-22 doi: 10.6046/gtzyyg.2014.04.03

    Li Quanwen, Zhao Weilin, Yang Xiaomei, et al. FAST Algorithm-Based Geometric Accurate Rectification of Large HJ Satellite Image[J].Remote Sensing for Land & Resources, 2014, 26(4):14-22 doi: 10.6046/gtzyyg.2014.04.03
  • Related Articles

    [1]XU Kaiqiu, GONG Yan, FANG Shenghui, WANG Taoyang. Geometric Correction of Thermal Infrared Remote Sensing Image Assisted by High-Resolution Data[J]. Geomatics and Information Science of Wuhan University, 2020, 45(3): 426-431. DOI: 10.13203/j.whugis20180089
    [2]XIAO Xiongwu, LI Deren, GUO Bingxuan, JIANG Wanshou, ZANG Yufu, LIU Jianchen. A Robust and Rapid Viewpoint-Invariant Matching Method for Oblique Images[J]. Geomatics and Information Science of Wuhan University, 2016, 41(9): 1151-1159. DOI: 10.13203/j.whugis20140405
    [3]LI Jiancheng, XU Xinyu, ZHAO Yongqi, WAN Xiaoyun. Approach for Determining Satellite Gravity Model from GOCE Gravitational Gradient Tensor Invariant Observations[J]. Geomatics and Information Science of Wuhan University, 2016, 41(1): 21-26. DOI: 10.13203/j.whugis20150554
    [4]he peipei,  wan youchuan,  gao xianjun,  qin jiaxin. street  view images matching al gorithm based on colorscale-invariant  feature transform[J]. Geomatics and Information Science of Wuhan University, 2014, 39(7): 867-872.
    [5]yao guobiao,  deng kazhong, ai haibin,  du quanye. an al gorithm of automatic quasi-dense matching and three-dimensionalreconstruction for oblique  stereo  images[J]. Geomatics and Information Science of Wuhan University, 2014, 39(7): 843-849.
    [6]LIANG Yan, SHENG Yehua, ZHANG Ka, YANG Lin. Linear Feature Matching Method Based on Local Affine Invariant andEpipolar Constraint for Close-range Images[J]. Geomatics and Information Science of Wuhan University, 2014, 39(2): 229-233. DOI: 10.13203/j.whugis20120611
    [7]WU Jun, HE Guangjin, YAO Zexin, PENG Zhiyong, LI Jun, TANG Min. )Effective ASIFT Wide-baseline Stereo Image Matching Based onAdaptive Relative Affine Transformation Stimulation[J]. Geomatics and Information Science of Wuhan University, 2014, 39(2): 214-219. DOI: 10.13203/j.whugis20120557
    [8]CHENG Liang, GONG Jianya, HAN Peng, SONG Xiaogang. Automatic Optimization for Affine Invariant Feature Matching on Remote Sensing Imagery[J]. Geomatics and Information Science of Wuhan University, 2009, 34(4): 418-422.
    [9]HU Anwen, ZHANG Zuxun. Discussion on "Strict Geometric Model Based on Affine Transformation for Remote Sensing Image with High Resolution"[J]. Geomatics and Information Science of Wuhan University, 2006, 31(2): 104-107.
    [10]ZHANG Jianqing, ZHANG Zuxun. Strict Geometric Model Based on Affine Transformation for Remote Sensing Image with High Resolution[J]. Geomatics and Information Science of Wuhan University, 2002, 27(6): 555-559.
  • Cited by

    Periodical cited type(3)

    1. 张艺潇,赵忠国,郑江华. 利用MARS估算不同气象要素组合下的参考作物蒸散量. 武汉大学学报(信息科学版). 2022(05): 789-798 .
    2. 陈志坤,江俊君,姜鑫维,白露,蔡之华. 一种基于改进双边滤波的鲁棒高光谱遥感图像特征提取方法. 武汉大学学报(信息科学版). 2020(04): 504-510 .
    3. 薛晓琴,岳亚伟,夏磊,李丽,贺雄伟. 利用协同表示与神经网络的高光谱图像亚像元定位. 遥感信息. 2019(05): 69-75 .

    Other cited types(13)

Catalog

    Article views (1493) PDF downloads (218) Cited by(16)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return