图像拼接算法研究
|
摘要
图像拼接(image mosaic)技术是将一组相互间重叠部分的图像序列进行空间匹配对准,经重采样合成后形成一幅包含各图像序列信息的宽视角场景的、完整的、高清晰的新图像的技术。图像拼接在摄影测量学、计算机视觉、遥感图像处理、医学图像分析、计算机图形学等领域有着广泛的应用价值。
一般来说,图像拼接的过程由图像获取,图像配准,图像合成三步骤组成,其中图像配准是整个图像拼接的基础。本文研究了两种图像配准算法:基于特征和基于变换域的图像配准算法。
在基于特征的配准算法的基础上,提出一种稳健的基于特征点的配准算法。首先改进Harris角点检测算法,有效提高所提取特征点的速度和精度。然后利用相似测度NCC(normalized cross correlation——归一化互相关),通过用双向最大相关系数匹配的方法提取出初始特征点对,用随机采样法RANSAC(Random Sample Consensus)剔除伪特征点对,实现特征点对的精确匹配。最后用正确的特征点匹配对实现图像的配准。本文提出的算法适应性较强,在重复性纹理、旋转角度比较大等较难自动匹配场合下仍可以准确实现图像配准,实验证明该算法在配准方面具有良好的效果,算法准确率高,鲁棒性强,具有较高的使用价值。
基于变换域的算法是图像配准常使用的算法,它思路简单、具有一定程度的抵抗噪声,但很难处理镜头存在旋转和缩放的情况,同时搜索整个图像空间,计算代价高昂。本文研究了基于傅氏变换的图像配准方法,引入的极坐标变换方法可以将旋转角度、比例缩放因子转换为平移的形式,大大减少了计算量。实验结果表明了此方法的可行性和有效性。如果没有拼合处理,那么我们得到的拼接图像将会有明显的缝隙,所以必须寻找一个良好的合成算法以消除这种缝隙。本文提出了一种改进的渐入渐出的拼合算法对配准后图像合成,得到无缝的拼接图像,取得了满意的拼接效果。
Image mosaic is a technology that carries on the spatial matching to a series of image which are overlapped with each other, and finally builds a seamless and high quality image which has high resolution and big eyeshot. Image mosaic has widely applications in the fields of photogrammetry, computer vision, remote sensing image processing, medical image analysis, computer graphic and so on.
Generally speaking, the image mosaic process consists of the following steps. image acquisition, image registration, image fusion. Image registration is the important foundation of image mosaic.This article has studied two kind of image registration algorithm. feature-based image registration and FFT- based image registration algorithm.
Through researching the feature-based method, a robust image registration method is proposed. Firstly, corners are extracted using improved Harris operator which improves the precision and speed. Then, a normalized cross correlation and a kind of match method with bidirectional greatest correlative coefficient are used to extract the initial feature point pairs, and then the false feature point pairs are rejected by a Random Sample Consensus algorithm.Finally, the correct matching feature point pairs are used to realize the image registration. It has better registration results under a variety of conditions such as different light, bigger rotation and repetitive texture. The experiment results indicate this algorithm has good effect in image registration, high accurate rate, strong robustness, higher use value.
FFT-based phase correlation image registration algorithm is often used. Its theory is simple, realizes easily, resists noise, but time is long and difficult to process the rotation and zooming. This article has studied FFT-based image registration method, the polar coordinate transformation is introduced in order to make the rotation and zooming factor transform into the translation form, greatly reduce the computation. The experimental result has indicated this method feasibility and validity.
If without image fusion technology, the mosaic image will have the obvious seam, therefore must seek a good fusion method to eliminate this kind of seam. This article has proposed one kind of improvement gradually enters gradually leaves fusion method to realize the image mosaic. The algorithm has obtained the satisfactory blending effect.
一般来说,图像拼接的过程由图像获取,图像配准,图像合成三步骤组成,其中图像配准是整个图像拼接的基础。本文研究了两种图像配准算法:基于特征和基于变换域的图像配准算法。
在基于特征的配准算法的基础上,提出一种稳健的基于特征点的配准算法。首先改进Harris角点检测算法,有效提高所提取特征点的速度和精度。然后利用相似测度NCC(normalized cross correlation——归一化互相关),通过用双向最大相关系数匹配的方法提取出初始特征点对,用随机采样法RANSAC(Random Sample Consensus)剔除伪特征点对,实现特征点对的精确匹配。最后用正确的特征点匹配对实现图像的配准。本文提出的算法适应性较强,在重复性纹理、旋转角度比较大等较难自动匹配场合下仍可以准确实现图像配准,实验证明该算法在配准方面具有良好的效果,算法准确率高,鲁棒性强,具有较高的使用价值。
基于变换域的算法是图像配准常使用的算法,它思路简单、具有一定程度的抵抗噪声,但很难处理镜头存在旋转和缩放的情况,同时搜索整个图像空间,计算代价高昂。本文研究了基于傅氏变换的图像配准方法,引入的极坐标变换方法可以将旋转角度、比例缩放因子转换为平移的形式,大大减少了计算量。实验结果表明了此方法的可行性和有效性。如果没有拼合处理,那么我们得到的拼接图像将会有明显的缝隙,所以必须寻找一个良好的合成算法以消除这种缝隙。本文提出了一种改进的渐入渐出的拼合算法对配准后图像合成,得到无缝的拼接图像,取得了满意的拼接效果。
Image mosaic is a technology that carries on the spatial matching to a series of image which are overlapped with each other, and finally builds a seamless and high quality image which has high resolution and big eyeshot. Image mosaic has widely applications in the fields of photogrammetry, computer vision, remote sensing image processing, medical image analysis, computer graphic and so on.
Generally speaking, the image mosaic process consists of the following steps. image acquisition, image registration, image fusion. Image registration is the important foundation of image mosaic.This article has studied two kind of image registration algorithm. feature-based image registration and FFT- based image registration algorithm.
Through researching the feature-based method, a robust image registration method is proposed. Firstly, corners are extracted using improved Harris operator which improves the precision and speed. Then, a normalized cross correlation and a kind of match method with bidirectional greatest correlative coefficient are used to extract the initial feature point pairs, and then the false feature point pairs are rejected by a Random Sample Consensus algorithm.Finally, the correct matching feature point pairs are used to realize the image registration. It has better registration results under a variety of conditions such as different light, bigger rotation and repetitive texture. The experiment results indicate this algorithm has good effect in image registration, high accurate rate, strong robustness, higher use value.
FFT-based phase correlation image registration algorithm is often used. Its theory is simple, realizes easily, resists noise, but time is long and difficult to process the rotation and zooming. This article has studied FFT-based image registration method, the polar coordinate transformation is introduced in order to make the rotation and zooming factor transform into the translation form, greatly reduce the computation. The experimental result has indicated this method feasibility and validity.
If without image fusion technology, the mosaic image will have the obvious seam, therefore must seek a good fusion method to eliminate this kind of seam. This article has proposed one kind of improvement gradually enters gradually leaves fusion method to realize the image mosaic. The algorithm has obtained the satisfactory blending effect.
引文
[1]Brown L.G. A survey of image registration techniques. ACM Computing Survey.1992, 12, 24(4).326-376.
[2]Kuglin C.D, Mines D.C. The Phase Correlation Image Alignment Method. Proceedings of IEEE International Conference on Cybernetics and Society, New York.1975, 9.163-165.
[3]李弼程,彭天强,彭波等.智能图像处理技术.北京:电子工业出版社, 2004.260-286.
[4]Szeliski R. Video mosaics for virtual environments. IEEE Computer Graphics and Applications.1996, 3, 16(2).22-30.
[5]Meehan J. Panoramic Photography. London. Watson-Guptill Publications, 1990.
[6]Xiong Y, Turkowski Y. Creating image-based VR using a self-calibrating fisheye lens. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition.San Juan, Puerto Rico, USA, 1997.237-243.
[7]Peleg S, Rousso B. Mosaicing on Adaptive Manifolds. IEEE Trans. on Pattern and machine intelligence. 2000, 10, 22(10).1144-1154.
[8]Gumustekin S, Hall R.W. Mosaic image generation on a flattened Gaussian sphere. Proceedings 3rd IEEE Workshop on Applications of Computer Vision.1996, 12.50-55.
[9]Gumustekin S, Hall R.W. Image registration and mosaicing using a self-calibrating camera. In Proceedings of International Conference on mage Processing .1998, 10, 1.818-822.
[10]Shum H.Y, Szeliski R. Panoramic image mosaics. Technical Report MSR-TR-97-23, Microsoft research. l997.4-20.
[11]Szeliski R, Shum H.Y. Creating Full View Panoramic Image Mosaics and Environment Maps. In Proc. of ACM SIGGRAPH. 1997.251-258.
[12]Castro E.D, Morandi C. Registration of translated and rotated images using finite Fourier transforms, IEEE Trans. on Pattern Analysis and Machine Intelligence.1987, 9(5).700-703.
[13]Srinivasa R.B, Chatterji B.N. An FFT-based technique for translation, rotation, and scale-invariant image registration. IEEE Trans. on Image Processing.1996, 9, 8(5).1266-1271.
[14]Zokai S, Wolberg G. Image Registration Using Log-Polar Mappings for Recovery of Large-Scale Similarity and Projective Transformations. IEEE Trans. on Image Processing. 2005, 10, 14(10).1422-1434.
[15]Gupta R, Hartley R. Linear pushbroom cameras. IEEE Trans.On PAMI. 1997, 9, 19(9).963- 975.
[16]李忠新.图像镶嵌理论及若干算法研究.南京:南京理工大学博士论文.2004, 4.34-37.
[17]李志刚.边界重叠图像的一种快速拼接算法.计算机工程. 2000, 26(5).37-38.
[18]Rosenfeld A, Kak A.C. Digital Picture Processing. Vol I and II, Academic Press, Orlando, FL. 1982, 4.169-170.
[19]Kim D.H, Yoon Y.I, Cho J.S. A Quasi-feature Based Image Mosaic Algorithms Using Modified Block Matching Criteria.IEEE of Japan. 2001, 12-C(5).892-898.
[20]Ventura A.D, Rampini A, Schettini R. Image registration by recognition of corresponding structures. IEEE Trans. On Geoscience and Remote Sensing. 1990, 3, 28(3).305-314.
[21]Yang zhengwei, Cohen F.S. Cross-weighted moments and affine invariants for image registration and matching. IEEE Trans. on Pattern Analysis and Machine Intelligence. 1999, 8, 21(8).801-814.
[22]Chang S.H, Cheng F.H, Hsu W.H, etal. Fast algorithm for point pattern matching. invariant to translation, rotations and scale changes. Pattern Recognition.1997, 29(1).11-16.
[23]Li Hui, Manjunath B.S, Mitra S.K. A contour-based approach to multisensor image registration. IEEE Trans. on Image Processing. 1995, 3, 4(3).320-334.
[24]Yang zhengwei, Cohen F.S. Image registration and object recognition using affine invariants and convex hulls. IEEE Trans on Image Processing.1999, 8(7).934-946.
[25]张迁,刘政凯,庞彦伟等.基SUSAN算法的航空影像的自动配准.测绘学报. 2003, 8, 32 (3).245-250.
[26]Heng L.K, Hwa E.M, KOK H.S. High accuracy registration of translated and rotated images using hierarchical method. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. 2000,6.2211-2214.
[27]尚明姝,解凯.一种基于特征的全自动图像拼接算法.微计算机应用.2006.11, 27(6). 747-750.
[28]ChenS.E. QuickTime VR-An Image-Based Approach to Virtual Environment Navigation. Computer Graphics Proceedings. Annual Conference.1995.29-38.
[29]Nayar, Shree K, Karmarkaretal. 360×360 mosaics. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2000, 2.388-395.
[30]Chiang M.C, Boult T.E. Efficient image warping and super-resolution. In IEEE Workshop on Applications of Computer Vision (WACV' 96), Sarasota, Florida.1996, 12.56-61.
[31]Szeliski R. Image mosaicing for tele-reality applications. IEEE Computer Society on Applications of Computer Vision (WACV' 94), Sarasota, Florida. 1994, 12.44-53.
[32]Can A, Stewart C.V, Roysam B. A Feature-Based、Robust、Hierarchical Algorithm for Registering Pairs of Images of the Curved Human Retina. IEEE TRANSACtions on pattern analysis and machine intelligence. 2002, 3, 24(3).347-364.
[33]Ballard D.M, Brown C.M, Schunck B.G. Computer Vision. New York. McGraw-Hill.1987, 9.12-27.
[34]刘传才.图像理解与计算机视觉.厦门:厦门大学出版社, 2002.33-74.
[35]郑南宁.计算机视觉与模式识别.北京:国防工业出版社, 1998.14-20.
[36]马颂德,张正友.计算机视觉:计算理论与计算基础.上海:科学出版社, 1998.52-61.
[37]王俊杰,刘家茂,胡运发等.图像拼接技术.计算机科学.2003, 30(6). 141-144.
[38]Chen C.Y, Klette R.Image Stitching-Comparisons and New Techniques. CAIP.1999.615-622.
[39]McMillan L, Bishop G. Plenoptic modeling. An Image-based Rendering System. Computer Graphics Proceedings(SIGGRAPH).1995.39-46.
[40]Mann S, Picard R.W. Video orbits of the projective group. A simple approach to featureless estimation of parameters. IEEE Transactions on Image Processing. 1997, 9, 9 (6).1281-1295.
[41]Semple J.G , Kneebone G.T. Algebraic projective geometry. Oxford :Oxford University Press, 1952.
[42]罗述谦,周果宏.医学图像处理与分析.北京:科学出版社, 2003.
[43]Pergen J R, Burt P J, Hingorani R. Computing two motions from three frames. Proc 3 Int Conf Computer Vision.1990, 12.27-32.
[44]Svedlow M, McGillem C.D, Anuta P. E. Experimental examination of similarity measures and preprocessing methods used for image registration. Symposium on Machine Processing of Remotely Sensed Data, Purdue University, Indiana. 1976,6.4-9.
[45]Barnea D. I, Silverman H. F. A class of algorithms for fast digital image registration.IEEE Trans. on Computers, 1972, C-21(2).179-186.
[46]Thevenaz P, Unser M. Optimization of mutual information for multiresolution image registration. IEEE Trans. On Image Processing. 2000, 12, 9(12).2083-2099.
[47]Viola P.A, Wells W.M. Alignment by maximization of mutual information. Proc.5th Int.Conf.Computer Vision, Boston, MA. 1995, 6.16-23.
[48]Viola P.A, Wells W.M. Alignment by maximization of mutual information. Proc.5th Internation Journal of Computer Vision.1997, 24(2).137-154.
[49]Collignon A, Maes F, Vandermeulen D, etal. Automated multi-modality image registration based on information theory. Proc. of the Information Processing in Medical Imaging Conference, Norwell, MA.Kluwer.1995, 263-274.
[50]Maes F, Collignon A,Vandermeulen D, etal. Multimodality image registration by maximization of mutual information, IEEE Trans on Medical Imaging.1997, 4, 16.187-198.
[51]Robert L.G. Machine perception of three-dimension solids. Optical and Electro-Optimal Information Processing.Cambridge: MA.MIT Press.1965.159-197.
[52]Marr D. Vision-A computational investigation into the human representation and processing of visual information. W. H. Freeman and Company, San Francisco. 1982.
[53]Canny J. A computational approach to edge detection. IEEE Trans. on Pattern Analysis and Machine Intelligence. 1986, 11, 8(6).679-698.
[54]冈萨雷斯,伍兹.阮秋琦,阮宇智译.数字图像处理.第二版.北京:电子工业出版社.2003.460-507.
[55]Yang Zhengwei, Cohen F.S. Image registration and object recognition using affine invariants and convex hulls. IEEE Trans On Image Processing.1999, 7, 8(7).934-946.
[56]Dai X.L, Siamak K. Development of a feature-based Appoach to Automated Image Registration for Multitemporal and Multisensor Remotely Sensed Imagery. Proceedings of the 1997 IEEE International Geoscience and Remote Sensing Sysposium.1997.243-245.
[57]Goshtasby A, Stockman G.C, Page C.V. A region-based approach to digital image registration with subpixel accuracy. IEEE Trans. On Geoscience and Remote Sensing. 1986, 5, GE-24(3). 390-399.
[58]Copper J, Venkatesh S, Kitchen L. Early jump-out corner detectors. IEEE Trans on PAMI.1993, 8, 8(15).823-828.
[59]Lin R.S, Chu C.H, Hseh Y.C. A modified morphological corner detector. Pattern Recognition Letters.1998, 19(3).279-286.
[60]Moravec H.P. Towards Automatic Visual Obstacle Avoidance. Proceedings of the 5th International Joint Conference on Artificial Intelligence, Cambridge, Massachusetts. 1977.
[61]Harris C.G, Stephens M.J. A combined corner and edge detector. Processings Fourth Alvey Vision Conference. Manchester, U. K. 1988.147-151.
[62]Forstner W.A. A feature based correspondence algorithm for image matching. International Society for Photogrammetry and Remote Sensing, Rovaniemi, Finland.1986, 26-Ⅲ.
[63]Smith S. M, Brady J.M. SUSAN-A new approach to low level image processing. International Journal of Computer Vision. 1997, 1.45-78.
[64] Noble J. A. Finding corners. Image and Vision Computing.1988, (2).121-128.
[65]赵立初.角点匹配和亚像元定位算法研究.上海:上海交通大学, 2000.63-65.
[66]Marr D, Poggio T. A Computational Theory of Human Stereo Vision. Proc. Royal Soc. London.1979, 204.301-328.
[67]Stefano L.D, Mattoccia S, Mola M. An efficient algorithm for exhaustive Template matching based on normalized cross correlation. Proceedings of 12th Internatioal Conference on Image Image Analysis and Proceesing, Los Alamitos, CA, USA.2003, 9.322-327.
[68]Maiywan S, Kashyap R.L. A cluster based approach to Robust Regression and Outlier Detection. 1994 IEEE International Conference on Systems, Man and Cybernetics.1994, 10, 3.2561-2565.
[69]Peleg S, Herman J. Panoramic mosaics by manifold projection. Proceedings of the Conferenceon Computer Vision and Pattern Recognition, Los Alamitos. 1997, 6.338-343.
[70]Fischler M.A, Bolles R.C. Random sample consensus. A paradigm for model fitting with application to image analysis and automated cartography. Communications of the ACM. 1981, 24(6).381-395.
[71]Stewart C.V. MINPRAN, a new robust estimator for computer vision .IEEE Tran On Pattern Analysis and Machine Intelligence.1995,10,17(10).925-938.
[72]Hartley R, Zisserman A. Multiple View Geometry in Computer Vision. Cambridge:Cambridge University Press. 2003, 23-150.
[73]Alliney S, Morandi C. Digital Image Registration using Projections, IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-8.1986,3, 2.222-233.
[74]Alliney S. Spatial registration of multispectral and multitemporal digital imagery using fast-Fourier transform techniques. IEEE Trans. on Pattern Analysis and Machine Intelligence.1993, 15(5).499-504.
[75]Chen qinsheng, Defrise M, Deconnick F. Symmetric phase-only matched filtering of Fourier-mellin transform for image registration and recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence.1994, 12, 16(12).1156-1168.
[76]Wolberg G. Digital Image Warping. Los Alamitos California: IEEE Computer Society Press.1990.
[77]Jain R, Kasturi R, Schunck B.G. Machine Vision. New York. McGraw Hill. 1995.
[78]Hou H.S, Andrews H.C. Cubic splines for image interpolation and digital filtering. IEEE Trans. Acoust. Speech Signal Processing. 1978, 12, 26 (6).508-517.
[79]Unser M, Aldroubi A, Eden M. Fast B-spline transforms for continuous image resentation and interpolation, IEEE Trans. Pattern Anal. 1991, 3, 13 (3).277-285.
[80]Wittenbrink C.M, Somani A.K. 2D and 3D Optimal Parallel Image Warping. Proceedings of Seventh International Parallel Processing Symposium.1993, 4.331- 337.
[81]Weinhaus F, Wallerman M. A flexible approach to image warping. SPIE on Image Processing Algorithms and Techniques.1990, 1244.108-122.
[82]Smith S, Brady J. A new approach to low-level image processing. International Journal of Computer Vision. 1997, 23(1).45-78.
[83]Pert J.B, Edward H.A. A multiresolution spline with application to image mosaics. ACM Transactions on Graphics.1983, 10, 2(4).217-236.
[2]Kuglin C.D, Mines D.C. The Phase Correlation Image Alignment Method. Proceedings of IEEE International Conference on Cybernetics and Society, New York.1975, 9.163-165.
[3]李弼程,彭天强,彭波等.智能图像处理技术.北京:电子工业出版社, 2004.260-286.
[4]Szeliski R. Video mosaics for virtual environments. IEEE Computer Graphics and Applications.1996, 3, 16(2).22-30.
[5]Meehan J. Panoramic Photography. London. Watson-Guptill Publications, 1990.
[6]Xiong Y, Turkowski Y. Creating image-based VR using a self-calibrating fisheye lens. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition.San Juan, Puerto Rico, USA, 1997.237-243.
[7]Peleg S, Rousso B. Mosaicing on Adaptive Manifolds. IEEE Trans. on Pattern and machine intelligence. 2000, 10, 22(10).1144-1154.
[8]Gumustekin S, Hall R.W. Mosaic image generation on a flattened Gaussian sphere. Proceedings 3rd IEEE Workshop on Applications of Computer Vision.1996, 12.50-55.
[9]Gumustekin S, Hall R.W. Image registration and mosaicing using a self-calibrating camera. In Proceedings of International Conference on mage Processing .1998, 10, 1.818-822.
[10]Shum H.Y, Szeliski R. Panoramic image mosaics. Technical Report MSR-TR-97-23, Microsoft research. l997.4-20.
[11]Szeliski R, Shum H.Y. Creating Full View Panoramic Image Mosaics and Environment Maps. In Proc. of ACM SIGGRAPH. 1997.251-258.
[12]Castro E.D, Morandi C. Registration of translated and rotated images using finite Fourier transforms, IEEE Trans. on Pattern Analysis and Machine Intelligence.1987, 9(5).700-703.
[13]Srinivasa R.B, Chatterji B.N. An FFT-based technique for translation, rotation, and scale-invariant image registration. IEEE Trans. on Image Processing.1996, 9, 8(5).1266-1271.
[14]Zokai S, Wolberg G. Image Registration Using Log-Polar Mappings for Recovery of Large-Scale Similarity and Projective Transformations. IEEE Trans. on Image Processing. 2005, 10, 14(10).1422-1434.
[15]Gupta R, Hartley R. Linear pushbroom cameras. IEEE Trans.On PAMI. 1997, 9, 19(9).963- 975.
[16]李忠新.图像镶嵌理论及若干算法研究.南京:南京理工大学博士论文.2004, 4.34-37.
[17]李志刚.边界重叠图像的一种快速拼接算法.计算机工程. 2000, 26(5).37-38.
[18]Rosenfeld A, Kak A.C. Digital Picture Processing. Vol I and II, Academic Press, Orlando, FL. 1982, 4.169-170.
[19]Kim D.H, Yoon Y.I, Cho J.S. A Quasi-feature Based Image Mosaic Algorithms Using Modified Block Matching Criteria.IEEE of Japan. 2001, 12-C(5).892-898.
[20]Ventura A.D, Rampini A, Schettini R. Image registration by recognition of corresponding structures. IEEE Trans. On Geoscience and Remote Sensing. 1990, 3, 28(3).305-314.
[21]Yang zhengwei, Cohen F.S. Cross-weighted moments and affine invariants for image registration and matching. IEEE Trans. on Pattern Analysis and Machine Intelligence. 1999, 8, 21(8).801-814.
[22]Chang S.H, Cheng F.H, Hsu W.H, etal. Fast algorithm for point pattern matching. invariant to translation, rotations and scale changes. Pattern Recognition.1997, 29(1).11-16.
[23]Li Hui, Manjunath B.S, Mitra S.K. A contour-based approach to multisensor image registration. IEEE Trans. on Image Processing. 1995, 3, 4(3).320-334.
[24]Yang zhengwei, Cohen F.S. Image registration and object recognition using affine invariants and convex hulls. IEEE Trans on Image Processing.1999, 8(7).934-946.
[25]张迁,刘政凯,庞彦伟等.基SUSAN算法的航空影像的自动配准.测绘学报. 2003, 8, 32 (3).245-250.
[26]Heng L.K, Hwa E.M, KOK H.S. High accuracy registration of translated and rotated images using hierarchical method. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. 2000,6.2211-2214.
[27]尚明姝,解凯.一种基于特征的全自动图像拼接算法.微计算机应用.2006.11, 27(6). 747-750.
[28]ChenS.E. QuickTime VR-An Image-Based Approach to Virtual Environment Navigation. Computer Graphics Proceedings. Annual Conference.1995.29-38.
[29]Nayar, Shree K, Karmarkaretal. 360×360 mosaics. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2000, 2.388-395.
[30]Chiang M.C, Boult T.E. Efficient image warping and super-resolution. In IEEE Workshop on Applications of Computer Vision (WACV' 96), Sarasota, Florida.1996, 12.56-61.
[31]Szeliski R. Image mosaicing for tele-reality applications. IEEE Computer Society on Applications of Computer Vision (WACV' 94), Sarasota, Florida. 1994, 12.44-53.
[32]Can A, Stewart C.V, Roysam B. A Feature-Based、Robust、Hierarchical Algorithm for Registering Pairs of Images of the Curved Human Retina. IEEE TRANSACtions on pattern analysis and machine intelligence. 2002, 3, 24(3).347-364.
[33]Ballard D.M, Brown C.M, Schunck B.G. Computer Vision. New York. McGraw-Hill.1987, 9.12-27.
[34]刘传才.图像理解与计算机视觉.厦门:厦门大学出版社, 2002.33-74.
[35]郑南宁.计算机视觉与模式识别.北京:国防工业出版社, 1998.14-20.
[36]马颂德,张正友.计算机视觉:计算理论与计算基础.上海:科学出版社, 1998.52-61.
[37]王俊杰,刘家茂,胡运发等.图像拼接技术.计算机科学.2003, 30(6). 141-144.
[38]Chen C.Y, Klette R.Image Stitching-Comparisons and New Techniques. CAIP.1999.615-622.
[39]McMillan L, Bishop G. Plenoptic modeling. An Image-based Rendering System. Computer Graphics Proceedings(SIGGRAPH).1995.39-46.
[40]Mann S, Picard R.W. Video orbits of the projective group. A simple approach to featureless estimation of parameters. IEEE Transactions on Image Processing. 1997, 9, 9 (6).1281-1295.
[41]Semple J.G , Kneebone G.T. Algebraic projective geometry. Oxford :Oxford University Press, 1952.
[42]罗述谦,周果宏.医学图像处理与分析.北京:科学出版社, 2003.
[43]Pergen J R, Burt P J, Hingorani R. Computing two motions from three frames. Proc 3 Int Conf Computer Vision.1990, 12.27-32.
[44]Svedlow M, McGillem C.D, Anuta P. E. Experimental examination of similarity measures and preprocessing methods used for image registration. Symposium on Machine Processing of Remotely Sensed Data, Purdue University, Indiana. 1976,6.4-9.
[45]Barnea D. I, Silverman H. F. A class of algorithms for fast digital image registration.IEEE Trans. on Computers, 1972, C-21(2).179-186.
[46]Thevenaz P, Unser M. Optimization of mutual information for multiresolution image registration. IEEE Trans. On Image Processing. 2000, 12, 9(12).2083-2099.
[47]Viola P.A, Wells W.M. Alignment by maximization of mutual information. Proc.5th Int.Conf.Computer Vision, Boston, MA. 1995, 6.16-23.
[48]Viola P.A, Wells W.M. Alignment by maximization of mutual information. Proc.5th Internation Journal of Computer Vision.1997, 24(2).137-154.
[49]Collignon A, Maes F, Vandermeulen D, etal. Automated multi-modality image registration based on information theory. Proc. of the Information Processing in Medical Imaging Conference, Norwell, MA.Kluwer.1995, 263-274.
[50]Maes F, Collignon A,Vandermeulen D, etal. Multimodality image registration by maximization of mutual information, IEEE Trans on Medical Imaging.1997, 4, 16.187-198.
[51]Robert L.G. Machine perception of three-dimension solids. Optical and Electro-Optimal Information Processing.Cambridge: MA.MIT Press.1965.159-197.
[52]Marr D. Vision-A computational investigation into the human representation and processing of visual information. W. H. Freeman and Company, San Francisco. 1982.
[53]Canny J. A computational approach to edge detection. IEEE Trans. on Pattern Analysis and Machine Intelligence. 1986, 11, 8(6).679-698.
[54]冈萨雷斯,伍兹.阮秋琦,阮宇智译.数字图像处理.第二版.北京:电子工业出版社.2003.460-507.
[55]Yang Zhengwei, Cohen F.S. Image registration and object recognition using affine invariants and convex hulls. IEEE Trans On Image Processing.1999, 7, 8(7).934-946.
[56]Dai X.L, Siamak K. Development of a feature-based Appoach to Automated Image Registration for Multitemporal and Multisensor Remotely Sensed Imagery. Proceedings of the 1997 IEEE International Geoscience and Remote Sensing Sysposium.1997.243-245.
[57]Goshtasby A, Stockman G.C, Page C.V. A region-based approach to digital image registration with subpixel accuracy. IEEE Trans. On Geoscience and Remote Sensing. 1986, 5, GE-24(3). 390-399.
[58]Copper J, Venkatesh S, Kitchen L. Early jump-out corner detectors. IEEE Trans on PAMI.1993, 8, 8(15).823-828.
[59]Lin R.S, Chu C.H, Hseh Y.C. A modified morphological corner detector. Pattern Recognition Letters.1998, 19(3).279-286.
[60]Moravec H.P. Towards Automatic Visual Obstacle Avoidance. Proceedings of the 5th International Joint Conference on Artificial Intelligence, Cambridge, Massachusetts. 1977.
[61]Harris C.G, Stephens M.J. A combined corner and edge detector. Processings Fourth Alvey Vision Conference. Manchester, U. K. 1988.147-151.
[62]Forstner W.A. A feature based correspondence algorithm for image matching. International Society for Photogrammetry and Remote Sensing, Rovaniemi, Finland.1986, 26-Ⅲ.
[63]Smith S. M, Brady J.M. SUSAN-A new approach to low level image processing. International Journal of Computer Vision. 1997, 1.45-78.
[64] Noble J. A. Finding corners. Image and Vision Computing.1988, (2).121-128.
[65]赵立初.角点匹配和亚像元定位算法研究.上海:上海交通大学, 2000.63-65.
[66]Marr D, Poggio T. A Computational Theory of Human Stereo Vision. Proc. Royal Soc. London.1979, 204.301-328.
[67]Stefano L.D, Mattoccia S, Mola M. An efficient algorithm for exhaustive Template matching based on normalized cross correlation. Proceedings of 12th Internatioal Conference on Image Image Analysis and Proceesing, Los Alamitos, CA, USA.2003, 9.322-327.
[68]Maiywan S, Kashyap R.L. A cluster based approach to Robust Regression and Outlier Detection. 1994 IEEE International Conference on Systems, Man and Cybernetics.1994, 10, 3.2561-2565.
[69]Peleg S, Herman J. Panoramic mosaics by manifold projection. Proceedings of the Conferenceon Computer Vision and Pattern Recognition, Los Alamitos. 1997, 6.338-343.
[70]Fischler M.A, Bolles R.C. Random sample consensus. A paradigm for model fitting with application to image analysis and automated cartography. Communications of the ACM. 1981, 24(6).381-395.
[71]Stewart C.V. MINPRAN, a new robust estimator for computer vision .IEEE Tran On Pattern Analysis and Machine Intelligence.1995,10,17(10).925-938.
[72]Hartley R, Zisserman A. Multiple View Geometry in Computer Vision. Cambridge:Cambridge University Press. 2003, 23-150.
[73]Alliney S, Morandi C. Digital Image Registration using Projections, IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-8.1986,3, 2.222-233.
[74]Alliney S. Spatial registration of multispectral and multitemporal digital imagery using fast-Fourier transform techniques. IEEE Trans. on Pattern Analysis and Machine Intelligence.1993, 15(5).499-504.
[75]Chen qinsheng, Defrise M, Deconnick F. Symmetric phase-only matched filtering of Fourier-mellin transform for image registration and recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence.1994, 12, 16(12).1156-1168.
[76]Wolberg G. Digital Image Warping. Los Alamitos California: IEEE Computer Society Press.1990.
[77]Jain R, Kasturi R, Schunck B.G. Machine Vision. New York. McGraw Hill. 1995.
[78]Hou H.S, Andrews H.C. Cubic splines for image interpolation and digital filtering. IEEE Trans. Acoust. Speech Signal Processing. 1978, 12, 26 (6).508-517.
[79]Unser M, Aldroubi A, Eden M. Fast B-spline transforms for continuous image resentation and interpolation, IEEE Trans. Pattern Anal. 1991, 3, 13 (3).277-285.
[80]Wittenbrink C.M, Somani A.K. 2D and 3D Optimal Parallel Image Warping. Proceedings of Seventh International Parallel Processing Symposium.1993, 4.331- 337.
[81]Weinhaus F, Wallerman M. A flexible approach to image warping. SPIE on Image Processing Algorithms and Techniques.1990, 1244.108-122.
[82]Smith S, Brady J. A new approach to low-level image processing. International Journal of Computer Vision. 1997, 23(1).45-78.
[83]Pert J.B, Edward H.A. A multiresolution spline with application to image mosaics. ACM Transactions on Graphics.1983, 10, 2(4).217-236.