顾及灰度和梯度信息的多模态影像配准算法

doi:10.11947/j.AGCS.2018.20170368

测绘学报 ›› 2018, Vol. 47 ›› Issue (1): 71-81.doi: 10.11947/j.AGCS.2018.20170368

顾及灰度和梯度信息的多模态影像配准算法

闫利, 王紫琦, 叶志云

武汉大学测绘学院, 湖北武汉 430079

收稿日期:2017-07-04 修回日期:2017-11-13 出版日期:2018-01-20 发布日期:2018-02-05
作者简介:闫利(1966-),男,博士,教授,研究方向为摄影测量与遥感。E-mail:lyan@sgg.whu.edu.cn
基金资助:
国土资源部公益性行业科研专项经费资助项目（201511009）

Multimodal Image Registration Algorithm Considering Grayscale and Gradient Information

YAN Li, WANG Ziqi, YE Zhiyun

School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Received:2017-07-04 Revised:2017-11-13 Online:2018-01-20 Published:2018-02-05
Supported by:
The Special Scientific Research Fund of Land and Resource Public Welfare Profession of China (No. 201511009)

摘要/Abstract

摘要： 基于特征匹配的多模态影像配准方法无法达到像素级配准精度要求。本文研究了一种顾及灰度和梯度信息的多模态影像配准算法。基于马尔科夫随机场（MRF）的非参数化配准模型充分利用多模态影像的图像信息进行相似性测量，同时考虑了灰度及梯度统计信息，求解方法上对值空间进行离散化，提高收敛速度。通过3组多模态影像的配准试验，验证了该算法的可行性。试验表明：本文算法的配准效果优于基于人工刺点的多项式模型配准和只考虑灰度信息的多模态影像配准；与此同时，该算法对于较大形变的影像配准也具有一定的适用性。在空间精度方面，平均配准误差小于1个像素，最大配准误差小于2个像素。

关键词: 多模态影像, 梯度信息, 马尔科夫随机场, 离散优化, 非参数化配准

Abstract: Multimodal image registration method based on feature matching can't satisfy the demands of pixel level registration precision.This paper proposes a multimodal image registration algorithm considering grayscale and gradient information.The nonparametric registration model based on Markov random field (MRF) makes full use of the image information of multimodal image to measure the similarity which considers the grayscale and the gradient statistical information are considered,and the value space is discretized to improve the convergence speed.The algorithm is validated both qualitatively and quantitatively demonstrating its potentials on three groups of multimodal image registration experiments.The result indicates that the proposed algorithm is superior to the polynomial model registration based on manual selection and the multimodal image registration only with gray information only.At the same time,this algorithm has some applicability for multimodal image registration of large deformation.In terms of spatial accuracy,the average registration error is less than 1 pixel and the maximum registration error is less than 2 pixels.

Key words: multimodal image, gradient information, Markov random field, discrete optimization, non-parametric registration

中图分类号:

P237

闫利, 王紫琦, 叶志云. 顾及灰度和梯度信息的多模态影像配准算法[J]. 测绘学报, 2018, 47(1): 71-81.

YAN Li, WANG Ziqi, YE Zhiyun. Multimodal Image Registration Algorithm Considering Grayscale and Gradient Information[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(1): 71-81.

参考文献

[1] 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(9): 1-6. NI Guoqiang, LIU Qiong. Analysis and Prospect of Multi-Source Image Registration Techniques[J]. Opto-Electronic Engineering, 2004, 31(9): 1-6.
[2] BROWN L G. A Survey of Image Registration Techniques[J]. ACM Computing Surveys, 1992, 24(4): 325-376.
[3] FANBin, HUO Chunlei, PAN Chunhong, et al. Registration of Optical and SAR Satellite Images by Exploring the Spatial Relationship of the Improved SIFT[J]. IEEE Geoscience and Remote Sensing Letters, 2013,10(4):657-661.
[4] LI Hui, MANJUNATH B S,MITRA S K.A Contour-Based Approach to Multisensor Image Registration[J]. IEEE Transactions on Image Processing, 1995,4(3):320-334.
[5] LI HH,ZHOU Yitong.Automatic Visual/IR Image Registration[J]. Optical Engineering, 1996,35(2):391-400.
[6] 张迁, 刘政凯, 庞彦伟, 等. 基于SUSAN算法的航空影像的自动配准[J]. 测绘学报, 2003, 32(3): 245-250. ZHANG Qian, LIU Zhengkai, PANG Yanwei, et al. Automatic Registration of Aerophotos Based on SUSAN Operator[J]. Acta Geodaetica et Cartographica Sinica, 2003, 32(3): 245-250.
[7] KELMANA, SOFKA M,STEWART C V.Keypoint Descriptors for Matching Across Multiple Image Modalities and Non-Linear Intensity Variations[C]//Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition.Minneapolis, MN, USA: IEEE,2007.
[8] KOVESI P.Image Featuresfrom Phase Congruency[J]. Videre: Journal of Computer Vision Research, 1999,1(3): 1-26.
[9] WONG A,CLAUSI D A.AISIR: Automated Inter-Sensor/Inter-Band Satellite Image Registration Using Robust Complex Wavelet Feature Representations[J]. Pattern Recognition Letters, 2010,31(10):1160-1167.
[10] 罗楠, 孙权森, 耿蕾蕾, 等. 一种扩展SURF描述符及其在遥感图像配准中的应用[J]. 测绘学报, 2013, 42(3): 383-388. LUO Nan, SUN Quansen, GENG Leilei, et al. An Extended SURF Descriptor and Its Application in Remote Sensing Images Registration[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(3): 383-388.
[11] VIOLA P,WELLS Ⅲ W M. Alignment by Maximization of Mutual Information[J]. International Journal of Computer Vision,1997, 24(2):137-154.
[12] COLLIGNONA, MAES F, DELAERE D, et al. Automated Multi-Modality Image Registration based on Information Theory[C]//Proceedings of the International Conference on Information Processing in Medical Imaging.Ile de Berder, France:Kluwer Academic Publishers,1995.
[13] MAES F, COLLIGNON A, VANDERMEULEN D, et al. Multimodality Image Registration by Maximization of Mutual Information[J]. IEEE Transactions on Medical Imaging,1997, 16(2): 187-198.
[14] STUDHOLME C, HILL D L G, HAWKES D J. An Overlap Invariant Entropy Measure of 3D Medical Image Alignment[J]. Pattern Recognition,1999, 32(1):71-86.
[15] RUECKERT D,CLARKSONM J, HILL D L G, et al. Non-Rigid Registration Using Higher-Order Mutual Information[C]//Proceedings of SPIE-Medical Imaging 2000: Image Processing.San Diego, CA, United States: SPIE, 2000: 438-447.
[16] RUSSAKOFF D B, TOMASI C, ROHLFING T, et al. Image Similarity Using Mutual Information of Regions[C]//Proceedings of the 8th European Conference on Computer Vision.Prague, Czech Republic: Springer, 2004: 596-607.
[17] TOMAŽEVIČ D, LIKAR B, PERNUŠ F. Multi-Feature Mutual Information Image Registration[J]. Image Analysis and Stereology, 2012, 31(1): 43-53.
[18] WANG Fei, VEMURI B C. Non-Rigid Multi-Modal Image Registration Using Cross-CumulativeResidual Entropy[J]. International Journal of Computer Vision,2007, 74(2):201-215.
[19] FAN Xiaofeng. Automatic Registration of Multi-Modal Airborne Imagery[D].Rochester:Rochester Institute of Technology, 2011.
[20] 闫德勤, 刘彩凤, 刘胜蓝, 等. 大形变微分同胚图像配准快速算法[J]. 自动化学报, 2015, 41(8): 1461-1470. YAN Deqin, LIU Caifeng, LIU Shenglan, et al.A Fast Image Registration Algorithm for DiffeomorphicImage with Large Deformation[J]. ActaAutomaticaSinica, 2015, 41(8): 1461-1470.
[21] STREKALOVSKIY E, CREMERS D. Real-Time Minimization of the Piecewise Smooth Mumford-Shah Functional[C]//Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerland:Springer, 2014: 127-141.
[22] KOMODAKIS N, TZIRITAS G, PARAGIOS N. Performance vs Computational Efficiency for Optimizing Single and Dynamic MRFs: Setting the State of the Art with Primal-Dual Strategies[J]. Computer Vision and Image Understanding,2008, 112(1): 14-29.
[23] KARANTZALOSK, SOTIRAS A,PARAGIOS N. Efficient and Automated Multimodal Satellite Data Registration through MRFs and Linear Programming[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.Columbus, OH, USA: IEEE, 2014.
[24] 张剑清, 潘励, 王树根. 摄影测量学[M]. 2版. 武汉: 武汉大学出版社, 2009. ZHANG Jianqing, PAN Li, WANG Shugen. Photogrammetry[M]. 2nd ed. Wuhan: Wuhan University Press, 2009.
[25] HIRSCHMULLERH.Stereo Processing by Semiglobal Matching and Mutual Information[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008,30(2):328-341.
[26] KIM J, KOLMOGOROV V,ZABIH R. Visual Correspondence Using Energy Minimization and Mutual Information[C]//Proceedings of the 9th IEEE International Conference on Computer Vision. Nice, France: IEEE, 2003.
[27] CHOIY,LEE S.Injectivity Conditions of 2D and 3D Uniform Cubic B-Spline Functions[J]. Graphical Models, 2000,62(6):411-427.

顾及灰度和梯度信息的多模态影像配准算法

Multimodal Image Registration Algorithm Considering Grayscale and Gradient Information

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价

[1]	袁博. 基于混合像元空间与谱间相关性模型的NMF线性盲解混[J]. 测绘学报, 2019, 48(9): 1151-1160.
[2]	南轲, 齐华, 叶沅鑫. 深度卷积特征表达的多模态遥感影像模板匹配方法[J]. 测绘学报, 2019, 48(6): 727-736.
[3]	杨红磊,彭军还. 基于马尔科夫随机场与模糊C均值的遥感影像分类[J]. 测绘学报, 2012, 41(2): 213-218.