Acta Geodaetica et Cartographica Sinica >

2018 , Vol. 47 >Issue 1: 71 - 81

DOI: https://doi.org/10.11947/j.AGCS.2018.20170368

Multimodal Image Registration Algorithm Considering Grayscale and Gradient Information

  • YAN Li ,
  • WANG Ziqi ,
  • YE Zhiyun
Expand
  • School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Received date: 2017-07-04

  Revised date: 2017-11-13

  Online published: 2018-02-05

Supported by

The Special Scientific Research Fund of Land and Resource Public Welfare Profession of China (No. 201511009)

Fold

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

Cite this article

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 . DOI: 10.11947/j.AGCS.2018.20170368

References

[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.
Options
Outlines

/

〈 〉