基于特征点的图像配准技术研究
|
摘要
图像配准是图像应用处理中的关键技术之一,在许多应用领域已经有成功应用,同时随着应用的深入,对图像配准的适应性、配准精度和计算效率提出了更高的要求。针对这些需求,本文研究了基于特征点的图像配准技术,目的是解决存在复杂空间变换图像之间的快速、精确配准问题。具体研究内容包括以下四个方面。
1、特征点检测方法与性能分析
在分析小波变换多尺度特性的基础上,提出了基于小波变换的旋转与尺度不变特征点检测方法。对经典的Harris特征点检测方法、尺度不变特征变换(SIFT)方法和本文提出的小波变换尺度不变特征点检测方法在相同和不同波段图像中的主要性能进行了详细分析。
2、特征点相似性度量与对应技术
特征点对应技术包括特征点相似性度量和特征点对应搜索两部分。
特征点相似性度量方法测量两幅图像中特征点的相似性。本文将傅立叶-梅林变换图像匹配方法和图像迹变换应用到特征点相似度量方法中来,提出了两种快速的能适应大旋转角度和较大尺度变换的图像特征点相似度量方法。其中基于迹变换的特征点相似度量方法的计算效率已经超过了归一化互相关方法。
特征点对应搜索策略根据特征点相似度量结果按照一定的准则来选择有效的特征点对应,排除不可能的特征点对应。特征点对应技术是基于特征点图像配准技术中关键的步骤,文中给出了详细的实验分析与对比。
对于不同波段的图像,图像局部熵方法和局部标准差方法是常用的图像特征变换方法。本文改进了局部标准差方法,得到了对数局部标准差图像特征变换方法。针对不同波段图像,分析了特征点对应技术在图像特征变换域中的性能,实验结果表明对数局部标准差特征变换有着更好的特征点对应性能。
3、基于特征点的图像配准方法
分析了图像空间变换的模型;回顾了由四对坐标点对应计算图像透视变换关系的直接线性法和随机抽样一致性稳健估计算法(RANSAC)及它们在图像空间变换估计中的应用;给出了基于特征点的图像配准方法处理流程、配准精度分析方法和仿真实验结果;对于存在相似变换的图像配准,对利用局部特征点集快速实现配准开展了研究。
4、基于特征点图像配准方法的应用
图像配准有着很多的应用。文中详细分析了宽基线图像配准、不同波段图像配准、航拍摇摆图像拼接、景象匹配和全景图像处理等应用的独特性和难点,并分析了本文提出的基于特征点的图像配准方法在这些应用中的性能。
Image registration is one of the key technologies in image processing. This dissertation addresses the problem of image registration based on feature points in images. Detection of feature points, Correspondence of feature points, the image registration method based on feature points and applications of image registration are systematically studied in this thesis.
1、Detection of feature points and performance analysis
The detection algorithm for feature points in an image and its performance is studied. Based on wavelet transform, a rotation and scale invariant detection algorithm is presented. This method uses the multiscale properties of wavelet transform to solve the problem of scale invariant in feature points detection and employs the same way as Harris's method to obtain the rotation invariant. The experiment results demonstrate that our method has better scale invariant characteristic than SIFT method. A detailed performance comparison of different feature point detectors is given. The comparison is carried out on the same band of images and different bands of images.
2、Correspondence of feature points and performance analysis
Correspondence of feature points involves two aspects: point similarity measures and search strategy of correspondence.
Point similarity measures can measure similarity of individual image points. This thesis suggests two kinds of point similarity measures which are respectively based on Fourier-Mellin transform and trace transform of image. These two methods can deal with complex and unknown image transform. Remarkably, the point similarity measure based on Trace transform is very fast and robust and its efficiency is as high as the NCC method.
Based on the similarity of feature points, search strategy of correspondence selects efficient correspondences of feature points and excludes false correspondences according some rule. Correspondence of feature points is an important step in image registration based on feature points; also a detailed performance analysis is given.
Local entropy and local standard deviation of images are efficient feature transforms for different band images. This thesis improves on the local standard deviation and presents a method so called logarithmic local standard deviation. The correspondence of feature points for different band images in feature transform domain is given.
3、Image registration based on feature points
This thesis analyzes image transform model, reviews the direct linear transform for image projective transform parameter computing and RANSAC for robust estimation of image transform model. We also present the flow chart of image registration based on feature points and the results of simulation experiments. For image registration with similar transform, a fast and robust image registration method based on local feature points group is proposed.
4、Application of image registration based on feature points
There are many applications of image registration. We analyze the difficulties and characteristics of some typical applications, such as wide-baseline image registration, registration for different bands images, aerophotographical mosaic, scene matching and panorama for multiple images. The performance of our image registration technology is presented.
1、特征点检测方法与性能分析
在分析小波变换多尺度特性的基础上,提出了基于小波变换的旋转与尺度不变特征点检测方法。对经典的Harris特征点检测方法、尺度不变特征变换(SIFT)方法和本文提出的小波变换尺度不变特征点检测方法在相同和不同波段图像中的主要性能进行了详细分析。
2、特征点相似性度量与对应技术
特征点对应技术包括特征点相似性度量和特征点对应搜索两部分。
特征点相似性度量方法测量两幅图像中特征点的相似性。本文将傅立叶-梅林变换图像匹配方法和图像迹变换应用到特征点相似度量方法中来,提出了两种快速的能适应大旋转角度和较大尺度变换的图像特征点相似度量方法。其中基于迹变换的特征点相似度量方法的计算效率已经超过了归一化互相关方法。
特征点对应搜索策略根据特征点相似度量结果按照一定的准则来选择有效的特征点对应,排除不可能的特征点对应。特征点对应技术是基于特征点图像配准技术中关键的步骤,文中给出了详细的实验分析与对比。
对于不同波段的图像,图像局部熵方法和局部标准差方法是常用的图像特征变换方法。本文改进了局部标准差方法,得到了对数局部标准差图像特征变换方法。针对不同波段图像,分析了特征点对应技术在图像特征变换域中的性能,实验结果表明对数局部标准差特征变换有着更好的特征点对应性能。
3、基于特征点的图像配准方法
分析了图像空间变换的模型;回顾了由四对坐标点对应计算图像透视变换关系的直接线性法和随机抽样一致性稳健估计算法(RANSAC)及它们在图像空间变换估计中的应用;给出了基于特征点的图像配准方法处理流程、配准精度分析方法和仿真实验结果;对于存在相似变换的图像配准,对利用局部特征点集快速实现配准开展了研究。
4、基于特征点图像配准方法的应用
图像配准有着很多的应用。文中详细分析了宽基线图像配准、不同波段图像配准、航拍摇摆图像拼接、景象匹配和全景图像处理等应用的独特性和难点,并分析了本文提出的基于特征点的图像配准方法在这些应用中的性能。
Image registration is one of the key technologies in image processing. This dissertation addresses the problem of image registration based on feature points in images. Detection of feature points, Correspondence of feature points, the image registration method based on feature points and applications of image registration are systematically studied in this thesis.
1、Detection of feature points and performance analysis
The detection algorithm for feature points in an image and its performance is studied. Based on wavelet transform, a rotation and scale invariant detection algorithm is presented. This method uses the multiscale properties of wavelet transform to solve the problem of scale invariant in feature points detection and employs the same way as Harris's method to obtain the rotation invariant. The experiment results demonstrate that our method has better scale invariant characteristic than SIFT method. A detailed performance comparison of different feature point detectors is given. The comparison is carried out on the same band of images and different bands of images.
2、Correspondence of feature points and performance analysis
Correspondence of feature points involves two aspects: point similarity measures and search strategy of correspondence.
Point similarity measures can measure similarity of individual image points. This thesis suggests two kinds of point similarity measures which are respectively based on Fourier-Mellin transform and trace transform of image. These two methods can deal with complex and unknown image transform. Remarkably, the point similarity measure based on Trace transform is very fast and robust and its efficiency is as high as the NCC method.
Based on the similarity of feature points, search strategy of correspondence selects efficient correspondences of feature points and excludes false correspondences according some rule. Correspondence of feature points is an important step in image registration based on feature points; also a detailed performance analysis is given.
Local entropy and local standard deviation of images are efficient feature transforms for different band images. This thesis improves on the local standard deviation and presents a method so called logarithmic local standard deviation. The correspondence of feature points for different band images in feature transform domain is given.
3、Image registration based on feature points
This thesis analyzes image transform model, reviews the direct linear transform for image projective transform parameter computing and RANSAC for robust estimation of image transform model. We also present the flow chart of image registration based on feature points and the results of simulation experiments. For image registration with similar transform, a fast and robust image registration method based on local feature points group is proposed.
4、Application of image registration based on feature points
There are many applications of image registration. We analyze the difficulties and characteristics of some typical applications, such as wide-baseline image registration, registration for different bands images, aerophotographical mosaic, scene matching and panorama for multiple images. The performance of our image registration technology is presented.
引文
[1]Barbara Zitova,Jan Flusser.Image registration methods:a survey[J].Image and Vision Computing 21(2003),p977-1000.
[2]Fuh C S,Liu H B.Projection for pattern recognition[J].Image and Vision Computing,1998,16(11):677-687.
[3]孙远,周刚慧,赵立初,施鹏飞.灰度图像匹配的快速算法[J].上海交通大学学报,2000,34(5):702-704.
[4]Srinivasa B,Reddy,Chatterji B N.An FFT-Based Technique for Translation,Rotation,and Scale-Invariant Image Registration[J].IEEE Trans.On Image Processing,1996,5(8):1266-1271.
[5]De E C,Morandi C.Registration of translated and rotated images using finite Fourier transforms[J].IEEE Trans.On Pattern Analysis and Machine Intelligence,1987,9(5):700-703.
[6]P.Viola,W M.W ells,Alignment by maximization of mutual information[J],Int'I.Journal of Computer Vision,1997,24:137-154.
[7]R.K.Sharma,M.Pavel,Multisensor image registration[J],Proc.Society for Information Display,1997,ⅩⅩ Ⅷ:951-954.
[8]H.S.Sawhney,R.Kumar,True multi-image alignment and its applications to mosaicing and lens distortion correction[J],IEEE Trans.Pattern Analysis and Machine Intelligence,1999,21:235-243.
[9]F.Maes,A.Collignon,D.Vandermeulen,G Marchal,P.Suetens,Multimodality image registration by maximization of mutual information[J],IEEE Trans.Medical Imaging,1997,16:187-198.
[10]Wong R Y.Sequential Scene Matching Using Edge Features[J].IEEE Trans.on AES,1978,14(1):128-140.
[11]Mount David M,Netanyahu Nathan S,Moigne Jacqueline Le.Efficient Algorithm for Robust Feature Matching[J].Pattern Recognition,1999,32:17 - 38.
[12]Li,Manjunath and Mitra,A Contour-Based Approach to Multisensor Image Registration[J],IEEE Transactions on Image Processing,Vol.4,No.3,pp.320-334,March,1995.
[13]Zhen-Ming Yuan,Fei Wu,Yue-Ting Zhuang,Multi-sensor image registration using multi resolution shape analysis[J],浙江大学学报A(英文版),JOURNAL OF ZHEJIANG UNIVERSITY SCIENCE A2006 Vol.7 No.4 P.549-555.
[14]ThamJ Y,Ranganath S,Ranganath M,Kassim A A.A Nover Unrestricted Center2Biased Diamond Search Algorithmfor Block Motion Estimation[J].IEEE Trans. on Circuits System and Video Technology , 1998 (8) : 369 - 377.
[15] Dai XL , Khorram Siamak. Development of a Feature - Based Approach to Automated Image Registration for Multitemporal and Multisensor Remotely Sensed Imagery[C] . Proceedings of the 1997 IEEE International Geoscience and Remoth Sensing Symposium, 1997 : 243 - 245.
[16]A.Goshtasby, A symbolically-assisted approach to digital image registration with application in computer vision[D], Ph.D.dissertation, Dept. of Computer Science,Michigan State University, TR 83-013, 1983.
[17]J.Ton and A.K.Jain, Registering Landsat images by point matching[J], IEEE Trans.On Geoscience and Remote Sensing, 1989, 27:642-651.
[18] Moravec H P. Towards automatic visual obstacle avoidance[C]. Proceedings of the 5th International Joint Conference on Artificial Intelligence, 1977.
[19] Harris C, Stephens M A. Combined corner and edge detector[C]. Proceedings of the 4th Alvey Vision Conference, 1988.
[20] Vincent T, Laganiere R. Matching feature points for telerobotics[C]. 1st International Workshop on HAVE and their Applications, 2002.
[21] You J, Bhattacharya P A. Wavelet-based coarse-to-fine image matching scheme in a parallel virtual machine environment[J]. IEEE Trans on Image Processing, 2000,9(9): 1547-1559.
[22] D. G. Lowe. Object recognition from local scale-invariant features[J]. In ICCV,1999, 1150-1157.
[23] C. V. Stewart. Robust parameter estimation in computer vision[J]. SIAM Review,41(3):513-537,1999.
[24] M. Fischler and R. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[C], in Comm. of the ACM, 1981, Vol. 24, pp. 381-395.
[25] H.Asada and M.Brady. The curvature primal sketch[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,8(1):2-14,1986.
[26] F.Mokhtarian and A.Mackworth. Scale-based description and recognition of planar curves and two-dimensional shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(1):34-43,1986.
[27] F.Mokhtarian and R.Suomela. Robust image corner detection through curvature scale space[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,20(12):1376-1381,1998.
[28] R.Horaud,T.Skordas,and F.Veillon. Finding geometric and relational structures in an image[C]. In Proceedings of the 1st European Conference on Computer Vision,Antibes, France,pages 374-384,1990.
[29] K.Rohr. Recognizing corners by fitting parametric models[J]. International Journal of Computer Vision,9(3):213-230,1992.
[30] P.R.Beaudet.Rotationally invariant image operators[C].In Proceedings of the 4th International Joint Conference on Pattern Recognition, Tokyo, pages 579-583,1978.
[31] W. Forstner.A framework for low level feature extraction[C]. In Proceedings of the 3th European Conference on Computer Vision, Stockholm, Sweden,pages 383-394,1994.
[32] W.Forstner and E.Gulch. A fast operator for detection and precise location of distinct points, corners and centres of circular features[C]. In Intercommission Conference on Fast Processing of Photogrammetric Data, Interlaken, Switzerland,pages 281-305,1987.
[33] K. Mikolajczyk and C. Schmid. Scale & Affine Invariant Interest Point Detectors[J]. In International Journal on Computer Vision, 60(1):63—86, 2004.
[34] K. Mikolajczyk and C. Schmid, An affine invariant interest point detector[C]. In Proceedings of the 7~(th) European Conference on Computer Vision, Copenhagen,Denmark, vol. I, pp.128-142.
[35] K. Mikolajczyk and C. Schmid, Indexing Based on Scale Invariant Interest Points[C], Proc. Eighth Int'l Conf. Computer Vision, pp. 525-531, 2001.
[36] T. Lindeberg. Feature detection with automatic scale selection[J]. International Journal of Computer Vision, 30(2):79—116, 1998.
[37] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision[M],Cambridge University Press, 2000.
[38] J.G.Semple and G.T.Kneebone. Algebraic Projective Geometry[M]. Oxford Science Publication, 1952.
[39] Lindeberg, T. Scale-space theory. A basic tool for analysing structures at different scales[M]. Journal of Applied Statistics, 21(2):224-270, 1994.
[40] C. Schmid, R. Mohr, and C. Bauckhage. Evaluation of interest point detectors[J].International Journal of Computer Vision,37(2):151-172, 2000.
[41] Rodehorst.V. and Koschan.A. Comparison and Evaluation of Feature Point Detectors[C]. Proc. Of 5~(th) International Symposium Turkish-German Joint Geodetic Days TGJGD'06,8p,Berlin,March 2006.
[42] 胡广书.现代信号处理教程[M].北京:清华大学出版社, 2004.
[43] R. Fransens, C. Strecha, L. Van Gool, Multimodal and Multiband Image Registration using Mutual Information[C], ESA-EUSC 2004: Theory and Applications of Knowledge driven Image Information Mining, with focus on Earth Observation;EUSC, Madrid (Spain); March 17-18, 2004.
[44] Clark F. Olson, Image Registration by Aligning Entropies[C]. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 331-336,2001.
[45]H.Li and Y.T.Zhou.Automatic eo/ir sensor image registration[C].In IEEE Int.Conf.on Image Proc.,volume B,pages 161-164,1995.
[46]B.C.Vemuri,J.Liu,and J.L.Marroquin,Robust multimodal image registration using local frequency representation[J],in Proc.Information Processing in Medical Imaging(IPMI),Davis,CA,June 2001,pp.176-182.
[47]FEDOTOV,N.SHULGA,New Theory of Pattern Recognition on the Basis of Stochastic Geometry[C],in Proc.WSCG 2000,the 8-th International Conference in Central Europe on Computer Graphics,Visualisation and Digital Media 2000,Plzen,Czech Republic,Feb.2000.
[48]KADYROV,A.PETROU,The Trace Transform and Its Applications[J],IEEE Transactions on Pattern Analysis and Machine Intelligence,Vol.23,No.8,pp.811-828,August 2001.
[49]Jan Kybic,Daniel Smutek,Image Registration Accuracy Estimation Without Ground Truth Using Bootstrap[J].CVAMIA 2006:61-72.
[50]Efron,Tibshirani,An Introduction to the Bootstrap[C].Number 57 in Monographs on Statistics and Applied Probability.Chapman & Hall,CRC(1993).
[51]Zoubir,A.M.,Boashash,The bootstrap and its applications in signal processing[J].IEEE Signal Processing Magazine(1998) 56-76.
[52]Lisa Gottesfeld BROWN.A survey of image registration techniques[J].ACM Computing Surveys.Vol.24,No.4,December 1992:p325-376.
[53]胡志萍.图像特征提取、匹配和新视点图像生成技术研究[D].博士学位论文.大连理工大学,2005.
[54]Shmuel Peleg,Joshua Herman.Panoramic Mosaics by Manifold Projection[C].In CVPR97 Page:338.
[55]R.Y.Wong,E.L.Hall,Scene matching with invariant moments[J],Computer Graphics and Image Processing 8(1978) 16-24.
[56]Il-Kyun Jung;Lacroix,S.A robust interest points matching algorithm[J].Computer Vision,2001.ICCV 2001.Proceedings.Eighth IEEE International Conference on Volume 2,Issue,2001 Page(s):538 - 543 vol.2.
[57]X.Dai,S.Khorram,A feature-based image registration algorithm using improved chain-code representation combined with invariant moments[J],IEEE Transactions on Geoscience and Remote Sensing 37(1999) 2351-2362.
[58]Timor Kadir,Andrew Zisserman,Michael Brady,An Affine Invariant Salient Region Detector[C],European Conference on Computer Vision 2004(ECCV 2004),pp.228-241,2004.
[59]P.Rogelj,S.Kova and J.C.Gee,Point similarity measures for non-rigid registration of multi-modal data[J],Computer Vision and Image Understanding,vol.92,pp.112-140,2003.
[60]Clark F.Olson,Maximum-Likelihood Image Matching[J],IEEE Transactions on Pattern Analysis and Machine Intelligence,24(6):853-857,June 2002.
[61]Clark F.Olson,Maximum-Likelihood Template Matching[C].In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,volume 2,pages 52-57,2000.
[62]Jin-Sung Lee,Ju-Hyun Cho,Ho-Chel Shin,Hae-Chul Choi,Seong-Dae Kim,Multi-Sensor Alignment using Feature-based Edge Matching[C],Proceedings of 2002IEEK Autumn Conference:Signal Processing Society,pp.271-274,2002.11.
[63]J.Inglada and F.Adragna,Automatic Multi- Sensor Image Registration by Edge Matching using Genetic Algorithms[C],International Geoscience and Remote Sensing Symposium,pp.2313-2315,2001.
[64]Irani and Anandan,Robust Multi-Sensor Image Alignment[C],International Conference on Computer Vision,pp.959-966,1998.
[65]K.J.Dana and P.Anandan,Registration of visible and infrared images[C],In Proc.SPIE Conf.on Arch.,Hardware and FLIR in Auto.Targ.Rec.,pages 1-12,1993.
[66]R.Kumar,K.Dana,and P.Anandan,Frameless registeration of mr and ct volumetric data sets[C].In Proc.of the Workshop on Applications of Computer Vision Ⅱ,Sarasota,Fl.,1994.
[67]廖斌,陈尚锋,肖山竹,卢焕章.局部矩不变量轮廓图像角点检测[J].计算机应用,2006,Vol.26:121—122.
[68]Y.Sheng and J.Duvernoy,Circular-Fourier-radial-Mellin transform descriptors for pattern recognition[J],J.Opt.Soc.Am.A 3,885-888(1986).
[69]A.Roche,G.Malandain,X.Pennec,and N.Ayache.The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration[C].In Proc.MICCAI'98,pages 1115~⊥ 1124,Cambridge Massachusetts(USA).
[70]C.T.Meyer,et.al.Demonstrating the accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations[J].MediA.Vol.1.No.3,pp:195-206.
[71]C.H.Chapman,Generalized Radon transforms and slant stacks[J],Geophysical Journal of the Royal Astronomical Society,vol.66,pp.445-453,1981.
[72]Rey,M.T.,J.K.Tunaley,J.T.Folinsbee,P.A.Jahans,J.A.Dixon and M.R.Vant,Appplication of Radon transform techniques to wake detection in Seasat-A SAR images[J],IEEE Trans.Geoscience Rem.Sens.,vol.28,no.4,pp.553-560,1990.
[73]Lian Cai,Sidan Du.Rotation,Scale and Translation Invariant Image Watermarking Using Radon Transform and Fourier Transform[C],IEEE 6th CAS Symp.on Emerging Technologies: Mobiel and Wireless Comm. Shanghai, China,May 31-June 2,2004.
[74] S. R. Deans, The Radon Transform and Some of Its Applications[M]. New York:Wiley, 1983.
[75] K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors[C].In Proceedings of the Conference on Computer Vision and Pattern Recognition,Madison, Wisconsin,USA, June 2003.
[76] T. Tuytelaars and L. Van Gool. Wide baseline stereo matching based on local affmely invariant regions[C]. In The Eleventh British Machine Vision Conference,University of Bristol, UK, pages 412-425, 2000.
[77] J. Koenderink and A. van Doom. Representation of local geometry in the visual system[J]. Biological Cybernetics, 55:367-375, 1987.
[78] S. Lazebnik, C. Schmid, and J. Ponce. Sparse texture representation using affine-invariant neighborhoods[C]. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, USA, 2003.
[79] J. Matas, O. Chum, M. Urban, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions[C]. In Proceedings of the 13th British Machine Vision Conference,Cardiff, England, pages 384-393, 2002.
[80] J. P. W. Pluim, A. Maintz, and M. A. Viergever, Mutual information-based registration of medical images: A survey[J], IEEE Trans.Med. Imag., vol. 22, no. 8, pp.986-1004, August 2003.
[81] A. J. Fitch, A. Kadyrov, W. J. Christmas, and J. Kittler, Fast robust correlation[J],IEEE Trans. Image Processing, vol. 14, no. 8, pp. 1063-1073, August 2005.
[82] H.-M. Chen and P. K. Varshney, A pyramid approach for multimodality image registration based on mutual information[C], in Proc. Third Int. Conf. Information Fusion, 2000, July 2000, vol. 1, pp. MoD3 9-15.
[83] Maintz, J.B.A., Viergever, M.A, A survey of medical image registration[J]. Med.Image Anal. 2(1), 1-36 (1998).
[84] P. E. Anuta, Spatial registration of multispectral and multitemporal digital imagery using fast Fourier transform techniques[J], IEEE Trans. Geosci. Electron., vol. GE-8,no. 4, pp. 353-368, Oct. 1970.
[85] Fitch, A.J., Kadyrov, A.C.W., Kittler, J, Orientation correlation[C], In: British machine video conference (2002).
[86] M. Petrou and A. Kadyrov, Affine Invariant Features from the Trace Transform[J],IEEE Trans. Pattern Analysis and MachineIntelligence, vol. 26, no. 1, pp. 30-44, Jan.2004.
[87] A. della Ventura, A. Rampini, and R. Schettini, Image Registration by Recognition of Corresponding Structures[J], IEEE Trans. Geoscience and Remote Sensing, vol. 28,pp. 305-314, 1990.
[88] A. Kadyrov and M. Petrou, Object Signatures Invariant to Affine Distortions Derived from the Trace Transform[J], Image and Vision Computing, vol. 21, pp.1135-1143,2003.
[89] Q.Zheng,R.Chellappa, A computational vision approach to image registration[C],in Proc. International Conference on Pattern Recognition, pp. 193-197,September 1992.
[90] B. Dasgupta and B. N. Chatterji, Fourier Mellin transform based image matching algorithm[J], J. Inst. Electron. Telecommun. Eng., vol. 42, no. 1, pp. 3-9, 1996.
[91] L. Lucchese, G. M. Cortelazzo, and C. Monti., High resolution estimation of planar rotations based on Fourier transform and radial projections[J]. In Proc. ISCAS 97, volume 2, pages 1181-1184, June 1997.
[92] P. Thevenaz, M. Unser, A pyramid approach to sub-pixel image fusion based on mutual information[C], Proceedings of the IEEE Interantional Conference on Image Processing ICIP'96, Lausanne,Switzerland, 1996, pp. 265-268.
[93] P. Thevenaz, M. Unser, An efficient mutual information optimizer for multiresolution image registration[C], Proceedings of the IEEE International Conference on Image Processing ICIP'98, Chicago, IL,1998, pp. 833-837.
[94] P. The'venaz, U.E. Ruttimann, M. Unser, Iterative multiscale registration without landmarks[C], Proceedings of the IEEE International Confernece on Image Processing ICIP'95, Washington DC, 1995, pp. 228-231.
[95] O. Thepaut, K. Kpalma, J. Ronsin, Automatic registration of ERS and SPOT multisensor images in a data fusion context[C], Forest Ecology and Management 128(2000)93-100.
[96] M. Roux, Automatic registration of SPOT images and digitized maps[C],Proceedings of the IEEE International Conference on Image Processing ICIP'96,Lausanne, Switzerland, 1996, pp. 625-628.
[97] G. Stockman, S. Kopstein, S. Benett, Matching images to models for registration and object detection via clustering[J], IEEE Transactions on Pattern Analysis and Machine Intelligence 4 (1982) 229-241.
[98] H.S. Stone, J. le Moigne, M. McGuire, The translation sensitivity of wavelet-based registration[J], IEEE Transactions on Pattern Analysis and Machine Intelligence 21(1999) 1074-1081.
[99] C. Studholme, D.L.G. Hill, D.J. Hawkes, An overlap invariant entropy measure of 3D medical image alignment[J], Pattern Recognition 32 (1999) 71-86.
[100] A. Rangarajan, H. Chui, J.S. Duncan, Rigid point feature registration using mutual information[J], Medical Image Analysis 4 (1999) 1-17.
[101] H.S. Sawhney, R. Kumar, True multi-image alignment and its applications to mosaicing and lens distortion correction[J], IEEE Transactions on Pattern Analysis and Machine Intellingece 21(1999) 235-243.
[102]G.P.Penney,J.Weese,J.A.Little,P.Desmedt,D.L.G.Hill,D.J.Hawkes,A comparison of similarity measures for use in 2D-3D medical image registration[J],IEEE Transactions on Medical Imaging 17(1998) 586-595.
[103]L.Lucchese,G.Doretto,G.M.Cortelazzo,A frequency domain technique for range data registration[J],IEEE Transactions on Pattern Analysis and Machine Intelligence 24(2002) 1468-1484.
[104]H.Ogawa,Labeled point pattern matching by fuzzy relaxation[J],Pattern Recognition 17(1984) 569-573.
[105]K.E.Price,Relaxation matching techniques--a comparison[J],IEEE Transactions on Pattern Analysis and Machine Intellingence 7(1985) 617-623.
[106]M.Holm,Towards automatic rectification of satellite images using feature based matching[C],Proceedings of the International Geoscience and Remote Sensing Symposium IGARSS'91,Espoo,Finland,1991,pp.2439-2442.
[107]M.Holden,D.L.G.Hill,E.R.E.Denton,J.M.Jarosz,T.C.S.Cox,T.Rohlfing,J.Goodey,D.J.Hawkes,Voxel similarity measures for 3d serial mr brain image registration[J],IEEE Transactions on Medical Imaging 19(2000) 94-102.
[108]J.W.Hsieh,H.Y.M.Liao,K.C.Fan,M.T.Ko,A fast algorithm for image registration without predetermining correspondence[C],Proceedings of the International Conference on Pattern Recognition ICPR'96,Vienna,Austria,1996,pp.765-769.
[109]安如,金夏玲,王慧麟等.基于特征匹配的影像可匹配性研究[J].红外与激光工程,2005,Vol.34:469—473.
[110]吴晏,丁明跃.基于图像直方图的一维不变矩研究[J].华中理工大学学报,1996,Vol.24:66—68.
[111]严柏军,郑链等。基于不变矩特征匹配的快速目标检测算法[J].红外技术,2001,Vol.23:8-12.
[112]曹茂永,孙农亮,郁道银.用于模式识别的极半径不变矩[J].计算机学报.2004,Vol.27:860-864.
[113]刘雅轩,苏秀琴,王萍.一种基于局部投影熵的图像匹配新算法[J].光子学报.2004,Vol.33:105-108.
[114]王敬东,徐亦斌,沈春林.一种新的任意角度旋转的景象匹配方法[J].南京航空航天大学学报.2005,Vol.37:6-10.
[115]桑农,张天序.基于神经网络的边缘强度互相关匹配可信度分析[J].中国图象图形学报.2000,Vol.7:580-584.
[116]陈立法,崔春明.多波段实时红外图像信号采集处理系统的设计[J].红外与 毫米波学报.2001,Vol.20:464-466.
[117]冈萨雷斯.数字图像处理[M].北京:电子工业出版社,2003.
[118]孙即祥.数字图象处理[M].石家庄:河北教育出版社,1993.
[119]李弼程,彭天强等.智能图像处理技术[M].北京:电子工业出版社,2004.
[120]Thomas M.Cover.信息论基础[M].北京:机械工业出版社,2005.
[121]张祖勋,张剑清.数字摄影测量学[M].武汉:武汉大学出版社,1997.
[122]孙仲康,沈振康.数字图像处理及其应用[M].北京:国防工业出版社,1985.
[123]章毓晋.图像处理和分析[M],清华大学出版社,北京,1999.
[124]王海晖,彭嘉雄等.一种多传感器遥感图像的配准方法[J].华中科技大学学报.2002,Vol.30:1-3.
[2]Fuh C S,Liu H B.Projection for pattern recognition[J].Image and Vision Computing,1998,16(11):677-687.
[3]孙远,周刚慧,赵立初,施鹏飞.灰度图像匹配的快速算法[J].上海交通大学学报,2000,34(5):702-704.
[4]Srinivasa B,Reddy,Chatterji B N.An FFT-Based Technique for Translation,Rotation,and Scale-Invariant Image Registration[J].IEEE Trans.On Image Processing,1996,5(8):1266-1271.
[5]De E C,Morandi C.Registration of translated and rotated images using finite Fourier transforms[J].IEEE Trans.On Pattern Analysis and Machine Intelligence,1987,9(5):700-703.
[6]P.Viola,W M.W ells,Alignment by maximization of mutual information[J],Int'I.Journal of Computer Vision,1997,24:137-154.
[7]R.K.Sharma,M.Pavel,Multisensor image registration[J],Proc.Society for Information Display,1997,ⅩⅩ Ⅷ:951-954.
[8]H.S.Sawhney,R.Kumar,True multi-image alignment and its applications to mosaicing and lens distortion correction[J],IEEE Trans.Pattern Analysis and Machine Intelligence,1999,21:235-243.
[9]F.Maes,A.Collignon,D.Vandermeulen,G Marchal,P.Suetens,Multimodality image registration by maximization of mutual information[J],IEEE Trans.Medical Imaging,1997,16:187-198.
[10]Wong R Y.Sequential Scene Matching Using Edge Features[J].IEEE Trans.on AES,1978,14(1):128-140.
[11]Mount David M,Netanyahu Nathan S,Moigne Jacqueline Le.Efficient Algorithm for Robust Feature Matching[J].Pattern Recognition,1999,32:17 - 38.
[12]Li,Manjunath and Mitra,A Contour-Based Approach to Multisensor Image Registration[J],IEEE Transactions on Image Processing,Vol.4,No.3,pp.320-334,March,1995.
[13]Zhen-Ming Yuan,Fei Wu,Yue-Ting Zhuang,Multi-sensor image registration using multi resolution shape analysis[J],浙江大学学报A(英文版),JOURNAL OF ZHEJIANG UNIVERSITY SCIENCE A2006 Vol.7 No.4 P.549-555.
[14]ThamJ Y,Ranganath S,Ranganath M,Kassim A A.A Nover Unrestricted Center2Biased Diamond Search Algorithmfor Block Motion Estimation[J].IEEE Trans. on Circuits System and Video Technology , 1998 (8) : 369 - 377.
[15] Dai XL , Khorram Siamak. Development of a Feature - Based Approach to Automated Image Registration for Multitemporal and Multisensor Remotely Sensed Imagery[C] . Proceedings of the 1997 IEEE International Geoscience and Remoth Sensing Symposium, 1997 : 243 - 245.
[16]A.Goshtasby, A symbolically-assisted approach to digital image registration with application in computer vision[D], Ph.D.dissertation, Dept. of Computer Science,Michigan State University, TR 83-013, 1983.
[17]J.Ton and A.K.Jain, Registering Landsat images by point matching[J], IEEE Trans.On Geoscience and Remote Sensing, 1989, 27:642-651.
[18] Moravec H P. Towards automatic visual obstacle avoidance[C]. Proceedings of the 5th International Joint Conference on Artificial Intelligence, 1977.
[19] Harris C, Stephens M A. Combined corner and edge detector[C]. Proceedings of the 4th Alvey Vision Conference, 1988.
[20] Vincent T, Laganiere R. Matching feature points for telerobotics[C]. 1st International Workshop on HAVE and their Applications, 2002.
[21] You J, Bhattacharya P A. Wavelet-based coarse-to-fine image matching scheme in a parallel virtual machine environment[J]. IEEE Trans on Image Processing, 2000,9(9): 1547-1559.
[22] D. G. Lowe. Object recognition from local scale-invariant features[J]. In ICCV,1999, 1150-1157.
[23] C. V. Stewart. Robust parameter estimation in computer vision[J]. SIAM Review,41(3):513-537,1999.
[24] M. Fischler and R. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[C], in Comm. of the ACM, 1981, Vol. 24, pp. 381-395.
[25] H.Asada and M.Brady. The curvature primal sketch[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,8(1):2-14,1986.
[26] F.Mokhtarian and A.Mackworth. Scale-based description and recognition of planar curves and two-dimensional shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(1):34-43,1986.
[27] F.Mokhtarian and R.Suomela. Robust image corner detection through curvature scale space[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,20(12):1376-1381,1998.
[28] R.Horaud,T.Skordas,and F.Veillon. Finding geometric and relational structures in an image[C]. In Proceedings of the 1st European Conference on Computer Vision,Antibes, France,pages 374-384,1990.
[29] K.Rohr. Recognizing corners by fitting parametric models[J]. International Journal of Computer Vision,9(3):213-230,1992.
[30] P.R.Beaudet.Rotationally invariant image operators[C].In Proceedings of the 4th International Joint Conference on Pattern Recognition, Tokyo, pages 579-583,1978.
[31] W. Forstner.A framework for low level feature extraction[C]. In Proceedings of the 3th European Conference on Computer Vision, Stockholm, Sweden,pages 383-394,1994.
[32] W.Forstner and E.Gulch. A fast operator for detection and precise location of distinct points, corners and centres of circular features[C]. In Intercommission Conference on Fast Processing of Photogrammetric Data, Interlaken, Switzerland,pages 281-305,1987.
[33] K. Mikolajczyk and C. Schmid. Scale & Affine Invariant Interest Point Detectors[J]. In International Journal on Computer Vision, 60(1):63—86, 2004.
[34] K. Mikolajczyk and C. Schmid, An affine invariant interest point detector[C]. In Proceedings of the 7~(th) European Conference on Computer Vision, Copenhagen,Denmark, vol. I, pp.128-142.
[35] K. Mikolajczyk and C. Schmid, Indexing Based on Scale Invariant Interest Points[C], Proc. Eighth Int'l Conf. Computer Vision, pp. 525-531, 2001.
[36] T. Lindeberg. Feature detection with automatic scale selection[J]. International Journal of Computer Vision, 30(2):79—116, 1998.
[37] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision[M],Cambridge University Press, 2000.
[38] J.G.Semple and G.T.Kneebone. Algebraic Projective Geometry[M]. Oxford Science Publication, 1952.
[39] Lindeberg, T. Scale-space theory. A basic tool for analysing structures at different scales[M]. Journal of Applied Statistics, 21(2):224-270, 1994.
[40] C. Schmid, R. Mohr, and C. Bauckhage. Evaluation of interest point detectors[J].International Journal of Computer Vision,37(2):151-172, 2000.
[41] Rodehorst.V. and Koschan.A. Comparison and Evaluation of Feature Point Detectors[C]. Proc. Of 5~(th) International Symposium Turkish-German Joint Geodetic Days TGJGD'06,8p,Berlin,March 2006.
[42] 胡广书.现代信号处理教程[M].北京:清华大学出版社, 2004.
[43] R. Fransens, C. Strecha, L. Van Gool, Multimodal and Multiband Image Registration using Mutual Information[C], ESA-EUSC 2004: Theory and Applications of Knowledge driven Image Information Mining, with focus on Earth Observation;EUSC, Madrid (Spain); March 17-18, 2004.
[44] Clark F. Olson, Image Registration by Aligning Entropies[C]. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 331-336,2001.
[45]H.Li and Y.T.Zhou.Automatic eo/ir sensor image registration[C].In IEEE Int.Conf.on Image Proc.,volume B,pages 161-164,1995.
[46]B.C.Vemuri,J.Liu,and J.L.Marroquin,Robust multimodal image registration using local frequency representation[J],in Proc.Information Processing in Medical Imaging(IPMI),Davis,CA,June 2001,pp.176-182.
[47]FEDOTOV,N.SHULGA,New Theory of Pattern Recognition on the Basis of Stochastic Geometry[C],in Proc.WSCG 2000,the 8-th International Conference in Central Europe on Computer Graphics,Visualisation and Digital Media 2000,Plzen,Czech Republic,Feb.2000.
[48]KADYROV,A.PETROU,The Trace Transform and Its Applications[J],IEEE Transactions on Pattern Analysis and Machine Intelligence,Vol.23,No.8,pp.811-828,August 2001.
[49]Jan Kybic,Daniel Smutek,Image Registration Accuracy Estimation Without Ground Truth Using Bootstrap[J].CVAMIA 2006:61-72.
[50]Efron,Tibshirani,An Introduction to the Bootstrap[C].Number 57 in Monographs on Statistics and Applied Probability.Chapman & Hall,CRC(1993).
[51]Zoubir,A.M.,Boashash,The bootstrap and its applications in signal processing[J].IEEE Signal Processing Magazine(1998) 56-76.
[52]Lisa Gottesfeld BROWN.A survey of image registration techniques[J].ACM Computing Surveys.Vol.24,No.4,December 1992:p325-376.
[53]胡志萍.图像特征提取、匹配和新视点图像生成技术研究[D].博士学位论文.大连理工大学,2005.
[54]Shmuel Peleg,Joshua Herman.Panoramic Mosaics by Manifold Projection[C].In CVPR97 Page:338.
[55]R.Y.Wong,E.L.Hall,Scene matching with invariant moments[J],Computer Graphics and Image Processing 8(1978) 16-24.
[56]Il-Kyun Jung;Lacroix,S.A robust interest points matching algorithm[J].Computer Vision,2001.ICCV 2001.Proceedings.Eighth IEEE International Conference on Volume 2,Issue,2001 Page(s):538 - 543 vol.2.
[57]X.Dai,S.Khorram,A feature-based image registration algorithm using improved chain-code representation combined with invariant moments[J],IEEE Transactions on Geoscience and Remote Sensing 37(1999) 2351-2362.
[58]Timor Kadir,Andrew Zisserman,Michael Brady,An Affine Invariant Salient Region Detector[C],European Conference on Computer Vision 2004(ECCV 2004),pp.228-241,2004.
[59]P.Rogelj,S.Kova and J.C.Gee,Point similarity measures for non-rigid registration of multi-modal data[J],Computer Vision and Image Understanding,vol.92,pp.112-140,2003.
[60]Clark F.Olson,Maximum-Likelihood Image Matching[J],IEEE Transactions on Pattern Analysis and Machine Intelligence,24(6):853-857,June 2002.
[61]Clark F.Olson,Maximum-Likelihood Template Matching[C].In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,volume 2,pages 52-57,2000.
[62]Jin-Sung Lee,Ju-Hyun Cho,Ho-Chel Shin,Hae-Chul Choi,Seong-Dae Kim,Multi-Sensor Alignment using Feature-based Edge Matching[C],Proceedings of 2002IEEK Autumn Conference:Signal Processing Society,pp.271-274,2002.11.
[63]J.Inglada and F.Adragna,Automatic Multi- Sensor Image Registration by Edge Matching using Genetic Algorithms[C],International Geoscience and Remote Sensing Symposium,pp.2313-2315,2001.
[64]Irani and Anandan,Robust Multi-Sensor Image Alignment[C],International Conference on Computer Vision,pp.959-966,1998.
[65]K.J.Dana and P.Anandan,Registration of visible and infrared images[C],In Proc.SPIE Conf.on Arch.,Hardware and FLIR in Auto.Targ.Rec.,pages 1-12,1993.
[66]R.Kumar,K.Dana,and P.Anandan,Frameless registeration of mr and ct volumetric data sets[C].In Proc.of the Workshop on Applications of Computer Vision Ⅱ,Sarasota,Fl.,1994.
[67]廖斌,陈尚锋,肖山竹,卢焕章.局部矩不变量轮廓图像角点检测[J].计算机应用,2006,Vol.26:121—122.
[68]Y.Sheng and J.Duvernoy,Circular-Fourier-radial-Mellin transform descriptors for pattern recognition[J],J.Opt.Soc.Am.A 3,885-888(1986).
[69]A.Roche,G.Malandain,X.Pennec,and N.Ayache.The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration[C].In Proc.MICCAI'98,pages 1115~⊥ 1124,Cambridge Massachusetts(USA).
[70]C.T.Meyer,et.al.Demonstrating the accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations[J].MediA.Vol.1.No.3,pp:195-206.
[71]C.H.Chapman,Generalized Radon transforms and slant stacks[J],Geophysical Journal of the Royal Astronomical Society,vol.66,pp.445-453,1981.
[72]Rey,M.T.,J.K.Tunaley,J.T.Folinsbee,P.A.Jahans,J.A.Dixon and M.R.Vant,Appplication of Radon transform techniques to wake detection in Seasat-A SAR images[J],IEEE Trans.Geoscience Rem.Sens.,vol.28,no.4,pp.553-560,1990.
[73]Lian Cai,Sidan Du.Rotation,Scale and Translation Invariant Image Watermarking Using Radon Transform and Fourier Transform[C],IEEE 6th CAS Symp.on Emerging Technologies: Mobiel and Wireless Comm. Shanghai, China,May 31-June 2,2004.
[74] S. R. Deans, The Radon Transform and Some of Its Applications[M]. New York:Wiley, 1983.
[75] K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors[C].In Proceedings of the Conference on Computer Vision and Pattern Recognition,Madison, Wisconsin,USA, June 2003.
[76] T. Tuytelaars and L. Van Gool. Wide baseline stereo matching based on local affmely invariant regions[C]. In The Eleventh British Machine Vision Conference,University of Bristol, UK, pages 412-425, 2000.
[77] J. Koenderink and A. van Doom. Representation of local geometry in the visual system[J]. Biological Cybernetics, 55:367-375, 1987.
[78] S. Lazebnik, C. Schmid, and J. Ponce. Sparse texture representation using affine-invariant neighborhoods[C]. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, USA, 2003.
[79] J. Matas, O. Chum, M. Urban, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions[C]. In Proceedings of the 13th British Machine Vision Conference,Cardiff, England, pages 384-393, 2002.
[80] J. P. W. Pluim, A. Maintz, and M. A. Viergever, Mutual information-based registration of medical images: A survey[J], IEEE Trans.Med. Imag., vol. 22, no. 8, pp.986-1004, August 2003.
[81] A. J. Fitch, A. Kadyrov, W. J. Christmas, and J. Kittler, Fast robust correlation[J],IEEE Trans. Image Processing, vol. 14, no. 8, pp. 1063-1073, August 2005.
[82] H.-M. Chen and P. K. Varshney, A pyramid approach for multimodality image registration based on mutual information[C], in Proc. Third Int. Conf. Information Fusion, 2000, July 2000, vol. 1, pp. MoD3 9-15.
[83] Maintz, J.B.A., Viergever, M.A, A survey of medical image registration[J]. Med.Image Anal. 2(1), 1-36 (1998).
[84] P. E. Anuta, Spatial registration of multispectral and multitemporal digital imagery using fast Fourier transform techniques[J], IEEE Trans. Geosci. Electron., vol. GE-8,no. 4, pp. 353-368, Oct. 1970.
[85] Fitch, A.J., Kadyrov, A.C.W., Kittler, J, Orientation correlation[C], In: British machine video conference (2002).
[86] M. Petrou and A. Kadyrov, Affine Invariant Features from the Trace Transform[J],IEEE Trans. Pattern Analysis and MachineIntelligence, vol. 26, no. 1, pp. 30-44, Jan.2004.
[87] A. della Ventura, A. Rampini, and R. Schettini, Image Registration by Recognition of Corresponding Structures[J], IEEE Trans. Geoscience and Remote Sensing, vol. 28,pp. 305-314, 1990.
[88] A. Kadyrov and M. Petrou, Object Signatures Invariant to Affine Distortions Derived from the Trace Transform[J], Image and Vision Computing, vol. 21, pp.1135-1143,2003.
[89] Q.Zheng,R.Chellappa, A computational vision approach to image registration[C],in Proc. International Conference on Pattern Recognition, pp. 193-197,September 1992.
[90] B. Dasgupta and B. N. Chatterji, Fourier Mellin transform based image matching algorithm[J], J. Inst. Electron. Telecommun. Eng., vol. 42, no. 1, pp. 3-9, 1996.
[91] L. Lucchese, G. M. Cortelazzo, and C. Monti., High resolution estimation of planar rotations based on Fourier transform and radial projections[J]. In Proc. ISCAS 97, volume 2, pages 1181-1184, June 1997.
[92] P. Thevenaz, M. Unser, A pyramid approach to sub-pixel image fusion based on mutual information[C], Proceedings of the IEEE Interantional Conference on Image Processing ICIP'96, Lausanne,Switzerland, 1996, pp. 265-268.
[93] P. Thevenaz, M. Unser, An efficient mutual information optimizer for multiresolution image registration[C], Proceedings of the IEEE International Conference on Image Processing ICIP'98, Chicago, IL,1998, pp. 833-837.
[94] P. The'venaz, U.E. Ruttimann, M. Unser, Iterative multiscale registration without landmarks[C], Proceedings of the IEEE International Confernece on Image Processing ICIP'95, Washington DC, 1995, pp. 228-231.
[95] O. Thepaut, K. Kpalma, J. Ronsin, Automatic registration of ERS and SPOT multisensor images in a data fusion context[C], Forest Ecology and Management 128(2000)93-100.
[96] M. Roux, Automatic registration of SPOT images and digitized maps[C],Proceedings of the IEEE International Conference on Image Processing ICIP'96,Lausanne, Switzerland, 1996, pp. 625-628.
[97] G. Stockman, S. Kopstein, S. Benett, Matching images to models for registration and object detection via clustering[J], IEEE Transactions on Pattern Analysis and Machine Intelligence 4 (1982) 229-241.
[98] H.S. Stone, J. le Moigne, M. McGuire, The translation sensitivity of wavelet-based registration[J], IEEE Transactions on Pattern Analysis and Machine Intelligence 21(1999) 1074-1081.
[99] C. Studholme, D.L.G. Hill, D.J. Hawkes, An overlap invariant entropy measure of 3D medical image alignment[J], Pattern Recognition 32 (1999) 71-86.
[100] A. Rangarajan, H. Chui, J.S. Duncan, Rigid point feature registration using mutual information[J], Medical Image Analysis 4 (1999) 1-17.
[101] H.S. Sawhney, R. Kumar, True multi-image alignment and its applications to mosaicing and lens distortion correction[J], IEEE Transactions on Pattern Analysis and Machine Intellingece 21(1999) 235-243.
[102]G.P.Penney,J.Weese,J.A.Little,P.Desmedt,D.L.G.Hill,D.J.Hawkes,A comparison of similarity measures for use in 2D-3D medical image registration[J],IEEE Transactions on Medical Imaging 17(1998) 586-595.
[103]L.Lucchese,G.Doretto,G.M.Cortelazzo,A frequency domain technique for range data registration[J],IEEE Transactions on Pattern Analysis and Machine Intelligence 24(2002) 1468-1484.
[104]H.Ogawa,Labeled point pattern matching by fuzzy relaxation[J],Pattern Recognition 17(1984) 569-573.
[105]K.E.Price,Relaxation matching techniques--a comparison[J],IEEE Transactions on Pattern Analysis and Machine Intellingence 7(1985) 617-623.
[106]M.Holm,Towards automatic rectification of satellite images using feature based matching[C],Proceedings of the International Geoscience and Remote Sensing Symposium IGARSS'91,Espoo,Finland,1991,pp.2439-2442.
[107]M.Holden,D.L.G.Hill,E.R.E.Denton,J.M.Jarosz,T.C.S.Cox,T.Rohlfing,J.Goodey,D.J.Hawkes,Voxel similarity measures for 3d serial mr brain image registration[J],IEEE Transactions on Medical Imaging 19(2000) 94-102.
[108]J.W.Hsieh,H.Y.M.Liao,K.C.Fan,M.T.Ko,A fast algorithm for image registration without predetermining correspondence[C],Proceedings of the International Conference on Pattern Recognition ICPR'96,Vienna,Austria,1996,pp.765-769.
[109]安如,金夏玲,王慧麟等.基于特征匹配的影像可匹配性研究[J].红外与激光工程,2005,Vol.34:469—473.
[110]吴晏,丁明跃.基于图像直方图的一维不变矩研究[J].华中理工大学学报,1996,Vol.24:66—68.
[111]严柏军,郑链等。基于不变矩特征匹配的快速目标检测算法[J].红外技术,2001,Vol.23:8-12.
[112]曹茂永,孙农亮,郁道银.用于模式识别的极半径不变矩[J].计算机学报.2004,Vol.27:860-864.
[113]刘雅轩,苏秀琴,王萍.一种基于局部投影熵的图像匹配新算法[J].光子学报.2004,Vol.33:105-108.
[114]王敬东,徐亦斌,沈春林.一种新的任意角度旋转的景象匹配方法[J].南京航空航天大学学报.2005,Vol.37:6-10.
[115]桑农,张天序.基于神经网络的边缘强度互相关匹配可信度分析[J].中国图象图形学报.2000,Vol.7:580-584.
[116]陈立法,崔春明.多波段实时红外图像信号采集处理系统的设计[J].红外与 毫米波学报.2001,Vol.20:464-466.
[117]冈萨雷斯.数字图像处理[M].北京:电子工业出版社,2003.
[118]孙即祥.数字图象处理[M].石家庄:河北教育出版社,1993.
[119]李弼程,彭天强等.智能图像处理技术[M].北京:电子工业出版社,2004.
[120]Thomas M.Cover.信息论基础[M].北京:机械工业出版社,2005.
[121]张祖勋,张剑清.数字摄影测量学[M].武汉:武汉大学出版社,1997.
[122]孙仲康,沈振康.数字图像处理及其应用[M].北京:国防工业出版社,1985.
[123]章毓晋.图像处理和分析[M],清华大学出版社,北京,1999.
[124]王海晖,彭嘉雄等.一种多传感器遥感图像的配准方法[J].华中科技大学学报.2002,Vol.30:1-3.