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基于有向点和有向线段的图像匹配算法研究
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摘要
图像匹配是集成电路制造装备研发的关键技术之一,为了满足集成电路制造的高密度、微尺度、大批量的要求,视觉自动对准系统成为其必不可少的组成部分,而图像匹配算法又是对准软件的核心所在。传统的基于灰度的图像匹配算法计算量大而且对光照变化敏感,为了提高匹配的速度、精度以及稳定性,本论文详细地研究了基于有向点和有向线段的图像匹配算法,并且使用C++实现了一个完整的图像匹配程序,可以快速精确地匹配模板图像和目标图像,主要研究内容和成果如下:
     1)把边缘点的坐标和梯度方向联合起来构成了有向点特征,并且推广到直线段,把直线段的端点坐标和直线段的梯度方向联合起来构成了有向线段特征。提出了一种几何滤波算法,利用直线拟合对亚像素级有向点进行滤波除噪,使得亚像素级有向点的坐标和方向更加稳定。对有向线段也进行了几何滤波,使得有向线段不受边缘链段中离群点的影响。
     2)以像素级有向点为特征定义了一种稳定的相似性度量,在搜索变换参数的过程中,利用相似性度量阈值和图像金字塔提高计算效率,并且提出了一种基于掩模的加速方法,根据目标图像中的有向点建立掩模,排除了不可能匹配的搜索区域,避免了在图像金字塔最顶层进行耗时的穷尽搜索,实现了模板图像和目标图像的快速粗略匹配。
     3)以亚像素级有向点为特征构造了一个新的匹配目标函数,不仅把斜切变换矩阵和缩放变换矩阵引入其中,扩展了适用范围,而且使用点-线距离作为误差度量,提高了匹配精度。为了快速求解目标函数,提出了一种点-线距离与点-点距离的等效转化方法,成功地将复杂的非线性优化问题转化为简单的线性优化问题,从而能够利用最小二乘法获得准确的解析解,实现了模板图像和目标图像的快速精确匹配。
     4)提出了一种基于点线对偶的图像匹配算法(Point-Line Duality,PLD),以有向线段为特征,利用点线对偶将其从(x-y)图像空间转换到(-)对偶空间,从而将直线匹配问题转化为点集匹配问题。提出了一种点融合的方法来处理原本属于同一条直线段的多条断开的直线段,增强了对偶点的稳定性,提高了对偶点集的匹配效率。提出了一种基于投票的点集匹配算法,能够快速地求解变换参数,并且定义了一种相似性度量来寻找所有对应的直线段,实现了模板图像和目标图像的快速粗略匹配。
Image matching is one of crucial techniques in the Integrated Circuit (IC)manufacturing equipments. As IC fabrication and packaging tend to be smaller, highlyintegrated, three-dimensional and massive, the vision based high precision alignmentsub-system is indispensable. However, the traditional area based image matching methodsare computationally expensive and sensitive to illumination changes. For the purpose ofimproving the performance of the alignment sub-system, the dissertation investigates theimage matching methods based on oriented point and oriented line segment systematically.And we develop a program for matching images fast and accurately using C++. The maincontributions are as follows:
     1) The coordinates and the gradient direction of an edge point are used to describe anew geometric feature named oriented point. And similarly, The coordinates ofendpoints and the gradient direction of a line segment are used to describe anothernew geometric feature named oriented line segment. A geometric denoisingmethod is proposed to make the oriented point more stable and to eliminate theinfluence of outliers on the oriented line segment.
     2) A robust similarity measure is defined by using the pixel level oriented points.During the search for transformation parameters, methods based on similaritymeasure threshold and image pyramids are used for computational efficiency. Amethod based on mask is proposed to avoid exhaustive search in the highest levelof image pyramid, where a mask is built according to the oriented points in thetarget image. The oriented point based image matching algorithm is able toquickly achieve coarse matching between images.
     3) A new objective function is built by using the subpixel level oriented points,which contains shearing matrix and scaling matrix. Also, the point to line error measure is used for better accuracy. However, the new objective function isnonlinear equation and this optimization problem is very time consuming. Toobtain the closed-form solution efficiently, the point to line error is equivalentlycomputed as the point to line error. The minimum point to line error based imagematching algorithm is able to quickly achieve fine matching between images.
     4) A Point-Line Duality (PLD) based method is proposed for image matching byusing the oriented line segments as feature. According to the fact that a linesegment in the image (x-y) space corresponds to a point in the dual (-) space,the line matching problem is converted to a point matching problem. For thepurpose of matching stability and computational efficiency, a point mergingalgorithm is proposed to deal with the fragmentary line segments that shouldbelong to a single line. A point pattern matching algorithm is proposed todetermine the transformation parameters, and the matched line pairs can also bereadily determined. The PLD image matching algorithm is able to quickly achievecoarse matching between images.
引文
[1]. L.G. Brown. A survey of image registration techniques. ACM Computing Surveys(CSUR).1992,24(4):325-376
    [2]. B. Zitova, J. Flusser. Image registration methods: a survey. Image and VisionComputing.2003,21(11):977-1000
    [3].王红梅,张科,李言俊.图像匹配研究进展.计算机工程与应用.2004,40(19):42-44
    [4].黎俊,彭启民,范植华.亚像素级图像配准算法研究.中国图象图形学报.2008,13(11):2071-2075
    [5]. C. Steger, M. Ulrich, C. Wiedemann. Machine Vision Algorithms and Applications.Wiley,2007
    [6]. D.I. Barnea, H.F. Silverman. A class of agorithms for fast digital image registration.IEEE Transactions on Computers.1972, C-21(2):179-186
    [7]. R.N. Bracewell. The Fourier Transform and Its Applications. McGraw Hill,2000
    [8]. E. De Castro, C. Morandi. Registration of translated and rotated images usingfinite fourier transforms. IEEE Transactions on Pattern Analysis and MachineIntelligence.1987,9(5):700-703
    [9]. B.S. Reddy, B.N. Chatterji. An FFT-based technique for translation, rotation, andscale-invariant image registration. IEEE Transactions on Image Processing.1996,5(8):1266-1271
    [10]. P. Viola, W.M. Wells. Alignment by maximization of mutual information.International Journal of Computer Vision.1997,24(2):137-154
    [11]. N. Ritter, R. Owens, J. Cooper, R.H. Eikelboom, P.P. van Saarloos. Registration ofstereo and temporal images of the retina. IEEE Transactions on Medical Imaging.1999,18(5):404-418
    [12]. D.G. Lowe. Object recognition from local scale-invariant features. In: Proceedingsof the7th IEEE International Conference on Computer Vision,1999:1150-1157vol.1152
    [13]. D.G. Lowe. Distinctive image features from scale-invariant keypoints. InternationalJournal of Computer Vision.2004,60(2):91-110
    [14]. H. Bay, T. Tuytelaars, L. Gool. SURF: Speeded Up Robust Features. In:Proceedings of the Computer Vision-ECCV2006--A. Leonardis, H. Bischof, A.Pinz, eds.: Springer Berlin Heidelberg,2006:404-417
    [15]. H. Bay, A. Ess, T. Tuytelaars, L. Van Gool. Speeded-Up Robust Features (SURF).Computer Vision and Image Understanding.2008,110(3):346-359
    [16]. C.T. Zahn, R.Z. Roskies. Fourier descriptors for plane closed curves. IEEETransactions on Computers.1972, C-21(3):269-281
    [17]. S. Belongie, J. Malik, J. Puzicha. Shape matching and object recognition usingshape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence.2002,24(4):509-522
    [18]. L. Wang, U. Neumann, S.Y. You. Wide-baseline image matching using linesignatures. In: Proceedings of the12th IEEE International Conference onComputer Vision,2009:1311-1318
    [19]. Z.H. Wang, F.C. Wu, Z.Y. Hu. MSLD: a robust descriptor for line matching.Pattern Recognition.2009,42(5):941-953
    [20]. M.-K. Hu. Visual pattern recognition by moment invariants. IRE Transactions onInformation Theory.1962,8(2):179-187
    [21]. J. Flusser, T. Suk. Pattern recognition by affine moment invariants. PatternRecognition.1993,26(1):167-174
    [22]. J. Matas, O. Chum, M. Urban, T. Pajdla. Robust wide baseline stereo frommaximally stable extremal regions. In: Proceedings of the British Machine VisionConference, BMVC2002Manchester, UK: British Machine Vision Assoc,2002:384-393
    [23]. J. Matas, O. Chum, M. Urban, T. Pajdla. Robust wide-baseline stereo frommaximally stable extremal regions. Image and Vision Computing.2004,22(10):761-767
    [24]. T. Lindeberg. Scale-space theory: a basic tool for analyzing structures at differentscales. Journal of Applied Statistics.1994,21(1-2):225-270
    [25]. H.P. Moravec. Rover visual obstacle avoidance. In: Proceedings of the7thInternational Joint Conference on Artificial Intelligence-Volume2, Vancouver,BC, Canada,1981:785-790
    [26]. C. Harris, M. Stephens. A combined corner and edge detector. In: Proceedings ofthe4th Alvey Vision Conference Manchester, UK,1988:147-151
    [27]. S. Smith, J.M. Brady. SUSAN-a new approach to low level image processing.International Journal of Computer Vision.1997,23(1):45-78
    [28]. M. Trajkovic, M. Hedley. Fast corner detection. Image and Vision Computing.1998,16(2):75-87
    [29].苏娟,林行刚,刘代志.一种基于结构特征边缘的多传感器图像配准方法.自动化学报.2009,35(3):251-257
    [30]. E. Rosten, R. Porter, T. Drummond. Faster and better: a machine learning approachto corner detection. IEEE Transactions on Pattern Analysis and MachineIntelligence.2010,32(1):105-119
    [31]. R.C. González, R.E. Woods. Digital Image Processing. Prentice Hall,2002
    [32]. D. Marr, E. Hildreth. Theory of edge detection. In: Proceedings of the RoyalSociety of London. Series B. Biological Sciences.1980,207(1167):187-217
    [33]. R.M. Haralick. Digital step edges from zero crossing of second directionalderivatives. IEEE Transactions on Pattern Analysis and Machine Intelligence.1984,6(1):58-68
    [34]. Q. Ji, R.M. Haralick. Efficient facet edge detection and quantitative performanceevaluation. Pattern Recognition.2002,35(3):689-700
    [35]. J. Canny. A computational approach to edge detection. IEEE Transactions onPattern Analysis and Machine Intelligence.1986,8(6):679-698
    [36].王慧燕.图像边缘检测和图像匹配研究及应用:[博士学位论文].杭州:浙江大学,2003
    [37]. E.P. Lyvers, O.R. Mitchell, M.L. Akey, A.P. Reeves. Subpixel measurements usinga moment-based edge operator. IEEE Transactions on Pattern Analysis andMachine Intelligence.1989,11(12):1293-1309
    [38]. S. Ghosal, R. Mehrotra. Orthogonal moment operators for subpixel edge detection.Pattern Recognition.1993,26(2):295-306
    [39]. Y.D. Qu, C.S. Cui, S.B. Chen, J.Q. Li. A fast subpixel edge detection method usingSobel-Zernike moments operator. Image and Vision Computing.2005,23(1):11-17
    [40]. T.J. Bin, A. Lei, C. Jiwen, K. Wenjing, L. Dandan. Subpixel edge location based onorthogonal Fourier-Mellin moments. Image and Vision Computing.2008,26(4):563-569
    [41]. K. Mikolajczyk, C. Schmid. An affine invariant interest point detector. In:Proceedings of the Computer Vison-ECCV2002, Pt1--A. Heyden, G. Sparr, M.Nielsen, P. Johansen, eds.,2002:128-142
    [42]. K. Mikolajczyk, C. Schmid. Scale&affine invariant interest point detectors.International Journal of Computer Vision.2004,60(1):63-86
    [43].刘立.基于多尺度特征的图像匹配与目标定位研究:[博士学位论文].武汉:华中科技大学,2008
    [44].丁南南.基于特征点的图像配准技术研究:[博士学位论文].长春:中国科学院研究生院(长春光学精密机械与物理研究所),2012
    [45]. S. Leutenegger, M. Chli, R.Y. Siegwart. BRISK: Binary Robust Invariant ScalableKeypoints. In: Proceedings of the IEEE International Conference on ComputerVision,2011:2548-2555
    [46]. L. Zhang, P. Bao. Edge detection by scale multiplication in wavelet domain.Pattern Recognition Letters.2002,23(14):1771-1784
    [47]. S. Konishi, A.L. Yuille, J.M. Coughlan, S.C. Zhu. Statistical edge detection:learning and evaluating edge cues. IEEE Transactions on Pattern Analysis andMachine Intelligence.2003,25(1):57-74
    [48]. Y. Ke, R. Sukthankar. PCA-SIFT: a more distinctive representation for local imagedescriptors. In: Proceedings of the IEEE Computer Society Conference onComputer Vision and Pattern Recognition, Vol2,2004:506-513
    [49]. K. Mikolajczyk, C. Schmid. A performance evaluation of local descriptors. IEEETransactions on Pattern Analysis and Machine Intelligence.2005,27(10):1615-1630
    [50]. S. Taylor, E. Rosten, T. Drummond. Robust feature matching in2.3mu s. In:Proceedings of the IEEE Computer Society Conference on Computer Vision andPattern Recognition Workshops,2009:493-500
    [51]. E. Tola, V. Lepetit, P. Fua. DAISY: an efficient dense descriptor applied towide-baseline stereo. IEEE Transactions on Pattern Analysis and MachineIntelligence.2010,32(5):815-830
    [52]. M. Calonder, V. Lepetit, C. Strecha, P. Fua. BRIEF: Binary Robust IndependentElementary Features. In: Proceedings of the Computer Vision-ECCV2010, PtIv--K. Daniilidis, P. Maragos, N. Paragios, eds.,2010:778-792
    [53]. M. Calonder, V. Lepetit, M. Oezuysal, T. Trzcinski, C. Strecha, P. Fua. BRIEF:computing a local binary descriptor very fast. IEEE Transactions on PatternAnalysis and Machine Intelligence.2012,34(7):1281-1298
    [54]. E. Rublee, V. Rabaud, K. Konolige, G. Bradski. ORB: an efficient alternative toSIFT or SURF. In: Proceedings of the IEEE International Conference on ComputerVision,2011:2564-2571
    [55]. G. Stockman, S. Kopstein, S. Benett. Matching images to models for registrationand object detection via clustering. IEEE Transactions on Pattern Analysis andMachine Intelligence.1982,4(3):229-241
    [56]. Y. Lamdan, H.J. Wolfson. Geometric Hashing: a general and efficient model-basedrecognition scheme. In: Proceedings of the2nd International Conference onComputer Vision,1988:238-249
    [57]. P.J. Besl, N.D. McKay. A method for registration of3-D shapes. IEEE Transactionson Pattern Analysis and Machine Intelligence.1992,14(2):239-256
    [58]. D.P. Huttenlocher, G.A. Klanderman, W.J. Rucklidge. Comparing images using theHausdorff distance. IEEE Transactions on Pattern Analysis and MachineIntelligence.1993,15(9):850-863
    [59]. S.H. Chang, F.H. Cheng, W.H. Hsu, G.Z. Wu. Fast algorithm for point patternmatching: Invariant to translations, rotations and scale changes. PatternRecognition.1997,30(2):311-320
    [60]. C. Steger. Similarity measures for occlusion, clutter, and illumination invariantobject recognition. In: Pattern Recognition--B. Radig, S. Florczyk, eds.: SpringerBerlin Heidelberg,2001:148-154
    [61]. S. Gold, A. Rangarajan, C.P. Lu, S. Pappu, E. Mjolsness. New algorithms for2Dand3D point matching: Pose estimation and correspondence. Pattern Recognition.1998,31(8):1019-1031
    [62]. H.L. Chui, A. Rangarajan. A new point matching algorithm for non-rigidregistration. Computer Vision and Image Understanding.2003,89(2-3):114-141
    [63]. A. Myronenko, X. Song, M.A. Carreira-Perpinan. Non-rigid point set registration:coherent point drift. In: Proceedings of the20th Annual Conference on NeuralInformation Processing Systems, NIPS2006Vancouver, BC, Canada: Neuralinformation processing system foundation,2007:1009-1016
    [64]. A. Myronenko, X. Song. Point set registration: coherent point drift. IEEETransactions on Pattern Analysis and Machine Intelligence.2010,32(12):2262-2275
    [65]. B. Jian, B.C. Vemuri. A robust algorithm for point set registration using mixture ofGaussians. In: Proceedings of the10th IEEE International Conference onComputer Vision, Vols1and2,2005:1246-1251
    [66]. B. Jian, B.C. Vemuri. Robust point set registration using Gaussian mixture models.IEEE Transactions on Pattern Analysis and Machine Intelligence.2011,33(8):1633-1645
    [67].廖斌.基于特征点的图像配准技术研究:[博士学位论文].长沙:国防科学技术大学,2008
    [68].高峰.图像配准中的几何特征不确定性建模及匹配方法研究:[博士学位论文].长沙:国防科学技术大学,2011
    [69].潘衡岳,王爱平,程志全,金士尧.结合图像特征和几何特征的级联图像匹配方法.系统仿真学报.2012,24(1):113-116
    [70]. R.O. Duda, P.E. Hart. Use of the Hough transformation to detect lines and curvesin pictures. Communications of the ACM.1972,15(1):11-15
    [71]. J. Matas, C. Galambos, J. Kittler. Robust detection of lines using the progressiveprobabilistic Hough transform. Computer Vision and Image Understanding.2000,78(1):119-137
    [72]. L.A.F. Fernandes, M.M. Oliveira. Real-time line detection through an improvedHough transform voting scheme. Pattern Recognition.2008,41(1):299-314
    [73]. U. Ramer. An iterative procedure for the polygonal approximation of plane curves.Computer Graphics and Image Processing.1972,1(3):244-256
    [74]. C. Schmid, A. Zisserman. Automatic line matching across views. In: Proceedingsof the IEEE Computer Society Conference on Computer Vision and PatternRecognition,1997:666-671
    [75]. C. Schmid, A. Zisserman. The geometry and matching of lines and curves overmultiple views. International Journal of Computer Vision.2000,40(3):199-233
    [76]. J.B. Burns, A.R. Hanson, E.M. Riseman. Extracting straight lines. IEEETransactions on Pattern Analysis and Machine Intelligence.1986,8(4):425-455
    [77]. R.G. von Gioi, J. Jakubowicz, J.-M. Morel, G. Randall. LSD: a fast line segmentdetector with a false detection control. IEEE Transactions on Pattern Analysis andMachine Intelligence.2010,32(4):722-732
    [78]. B. Khaleghi, M. Baklouti, F.O. Karray. SILT: Scale-Invariant Line Transform. In:Proceedings of the IEEE International Symposium on Computational Intelligencein Robotics and Automation,2009:78-83
    [79]. X.F. Ren, J. Malik. A probabilistic multi-scale model for contour completion basedon image statistics. In: Proceedings of the Computer Vison-ECCV2002, Pt1--A.Heyden, G. Sparr, M. Nielsen, P. Johansen, eds.,2002:312-327
    [80]. D.R. Martin, C.C. Fowlkes, J. Malik. Learning to detect natural image boundariesusing local brightness, color, and texture cues. IEEE Transactions on PatternAnalysis and Machine Intelligence.2004,26(5):530-549
    [81]. V. Ferrari, L. Fevrier, F. Jurie, C. Schmid. Groups of adjacent contour segments forobject detection. IEEE Transactions on Pattern Analysis and Machine Intelligence.2008,30(1):36-51
    [82]. B. Fan, F. Wu, Z. Hu. Line matching leveraged by point correspondences. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2010:390-397
    [83]. B. Fan, F. Wu, Z. Hu. Robust line matching through line-point invariants. PatternRecognition.2012,45(2):794-805
    [84]. D.H. Ballard. Generalizing the Hough transform to detect arbitrary shapes. PatternRecognition.1981,13(2):111-122
    [85]. G. Medioni, R. Nevatia. Matching images using linear features. IEEE Transactionson Pattern Analysis and Machine Intelligence.1984,6(6):675-685
    [86]. N. Ayache, O.D. Faugeras. HYPER: a new approach for the recognition andpositioning of two-dimensional objects. IEEE Transactions on Pattern Analysis andMachine Intelligence.1986,8(1):44-54
    [87]. C. Schmid, A. Zisserman. The geometry and matching of curves in multiple views.In: Computer Vision-ECCV'98--H. Burkhardt, B. Neumann, eds.: Springer BerlinHeidelberg,1998:394-409
    [88]. H. Bay, V. Ferrari, L. Van Gool. Wide-baseline stereo matching with line segments.In: Proceedings of the IEEE Computer Society Conference on Computer Visionand Pattern Recognition, Vol1, Proceedings--C. Schmid, S. Soatto, C. Tomasi, eds.,2005:329-336
    [89].张强,那彦,李建军.基于边缘几何特征和频域相关技术的图像匹配方法.应用光学.2006,27(4):285-288
    [90].张浩.多信息融合图像边缘特征提取及图像配准研究与应用:[博士学位论文].杭州:浙江大学,2008
    [91].李壮,朱宪伟.基于边缘相似性的异源图像匹配.飞行器测控学报.2011,30(2):37-41
    [92]. T. Kadir, M. Brady. Saliency, scale and image description. International Journal ofComputer Vision.2001,45(2):83-105
    [93]. T. Kadir, A. Zisserman, M. Brady. An affine invariant salient region detector. In:Proceedings of the Computer Vision-ECCV2004, Pt1--T. Pajdla, J. Matas, eds.,2004:228-241
    [94]. P.-E. Forssen, D.G. Lowe. Shape descriptors for maximally stable extremal regions.In: Proceedings of the11th IEEE International Conference on Computer Vision,Vols1-6,2007:1530-1537
    [95]. T. Tuytelaars, L. Van Gool. Matching widely separated views based on affineinvariant regions. International Journal of Computer Vision.2004,59(1):61-85
    [96].周武.精密图像配准方法研究及在精密电子组装中的应用:[博士学位论文]. G广州:华南理工大学,2012
    [97]. S. Tabbone, D. Ziou. Subpixel positioning of edges for first and second orderoperators. In: Proceedings of the11th IAPR International Conference on PatternRecognition, Vol.III. Conference C: Image, Speech and Signal Analysis,1992:655-658
    [98]. C. Steger. Subpixel-precise extraction of lines and edges. International Archives ofPhotogrammetry and Remote Sensing.2000,33(3):141-156
    [99]. V.S. Nalwa, T.O. Binford. On detecting edges. IEEE Transactions on PatternAnalysis and Machine Intelligence.1986,8(6):699-714
    [100]. M. Kisworo, S. Venkatesh, G. West. Modeling edges at subpixel accuracy using thelocal energy approach. IEEE Transactions on Pattern Analysis and MachineIntelligence.1994,16(4):405-410
    [101]. J. Ye, G.K. Fu, U.P. Poudel. High-accuracy edge detection with blurred edge model.Image and Vision Computing.2005,23(5):453-467
    [102]. P.J. Rousseeuw, A.M. Leroy. Robust Regression and Outlier Detection. John Wiley&Sons,2005
    [103]. G. Borgefors. Hierarchical chamfer matching: a parametric edge matchingalgorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence.1988,10(6):849-865
    [104]. C.F. Olson, D.P. Huttenlocher. Automatic target recognition by matching orientededge pixels. IEEE Transactions on Image Processing.1997,6(1):103-113
    [105]. W.J. Rucklidge. Efficiently locating objects using the Hausdorff distance.International Journal of Computer Vision.1997,24(3):251-270
    [106].牛力丕,毛士艺,陈炜.基于Hausdorff距离的图像配准研究.电子与信息学报.2007,29(1):35-38
    [107]. D.T. Nguyen, W.Q. Li, P. Ogunbona. An improved template matching method forobject detection. In: Proceedings of the Computer Vision-ACCV2009, Pt Iii--H.Zha, R.I. Taniguchi, S. Maybank, eds.,2010:193-202
    [108]. C. Steger. System and method for object recognition U.S. Patent7,062,093,2006
    [109]. S.L. Tanimoto. Template matching in pyramids. Computer Graphics and ImageProcessing.1981,16(4):356-369
    [110]. HALCON8.0Shape-Based Matching. MVTec Software GmbH,2007
    [111]. Y. Chen, G. Medioni. Object modelling by registration of multiple range images.Image and Vision Computing.1992,10(3):145-155
    [112]. Z. Zhang. Iterative point matching for registration of free-form curves and surfaces.International Journal of Computer Vision.1994,13(2):119-152
    [113]. S. Rusinkiewicz, M. Levoy. Efficient variants of the ICP algorithm. In:Proceedings of the3rd International Conference on3-D Digital Imaging andModeling,2001:145-152
    [114]. T. Jost, H. Hugli. A multi-resolution ICP with heuristic closest point search for fastand robust3D registration of range images. In: Proceedings of the4th InternationalConference on3-D Digital Imaging and Modeling--S. Kawada, ed.,2003:427-433
    [115]. M. Greenspan, M. Yurick. Approximate k-d tree search for efficient ICP. In:Proceedings of the4th International Conference on3-D Digital Imaging andModeling--S. Kawada, ed.,2003:442-448
    [116]. A. Nuchter, K. Lingemann, J. Hertzberg. Cached k-d tree search for ICP algorithms.In: Proceedings of the6th International Conference on3-D Digital Imaging andModeling--G. Godin, P. Hebert, T. Masuda, G. Taubin, eds.,2007:419-426
    [117]. J. Minguez, L. Montesano, F. Lamiraux. Metric-based iterative closest point scanmatching for sensor displacement estimation. IEEE Transactions on Robotics.2006,22(5):1047-1054
    [118]. A. Censi. An ICP variant using a point-to-line metric. In: Proceedings of the IEEEInternational Conference on Robotics and Automation, Vols1-9,2008:19-25
    [119]. A.W. Fitzgibbon. Robust registration of2D and3D point sets. Image and VisionComputing.2003,21(13–14):1145-1153
    [120]. D. Chetverikov, D. Stepanov, P. Krsek. Robust euclidean alignment of3D pointsets: the trimmed iterative closest point algorithm. Image and Vision Computing.2005,23(3):299-309
    [121]. H.B. Zha, M. Ikuta, T. Hasegawa. Registration of range images with differentscanning resolutions. In: Proceedings of the IEEE International Conference onSystems, Man&Cybernetics, Vol1-5,2000:1495-1500
    [122]. S.Y. Du, N.N. Zheng, L. Xiong, S.H. Ying, J.R. Xue. Scaling iterative closest pointalgorithm for registration of m-D point sets. Journal of Visual Communication andImage Representation.2010,21(5-6):442-452
    [123]. K. Voss, H. Suesse. Affine point pattern matching. In: Pattern Recognition--B.Radig, S. Florczyk, eds.: Springer Berlin Heidelberg,2001:155-162
    [124]. J. Ho, Y. Ming-Hsuan, A. Rangarajan, B. Vemuri. A new affine registrationalgorithm for matching2D point sets. In: Proceedings of the IEEE Workshop onApplications of Computer Vision,2007:25-25
    [125]. S.Y. Du, N.N. Zheng, G.F. Meng, Z.J. Yuan. Affine registration of point sets usingICP and ICA. IEEE Signal Processing Letters.2008,15:689-692
    [126]. S.Y. Du, N.N. Zheng, S.H. Ying, J.Y. Liu. Affine iterative closest point algorithmfor point set registration. Pattern Recognition Letters.2010,31(9):791-799
    [127]. S.H. Ying, Y.X. Peng, Z.J. Wen. Iwasawa decomposition: a new approach to2Daffine registration problem. Pattern Analysis and Applications.2011,14(2):127-137
    [128]. J. Zhu, S. Du, Z. Yuan, Y. Liu, L. Ma. Robust affine iterative closest pointalgorithm with bidirectional distance. IET Computer Vision.2012,6(3):252-261
    [129]. F. Lu, E. Milios. Robot pose estimation in unknown environments by matching2Drange scans. Journal of Intelligent&Robotic Systems.1997,18(3):249-275
    [130]. L.J. Latecki, R. Lakamper, U. Eckhardt. Shape descriptors for non-rigid shapeswith a single closed contour. In: Proceedings of the IEEE Conference on ComputerVision and Pattern Recognition, Vol I,2000:424-429
    [131]. M.W. Koch, R.L. Kashyap. Using polygons to recognize and locate partiallyoccluded objects. IEEE Transactions on Pattern Analysis and Machine Intelligence.1987,9(4):483-494
    [132]. Y. Li, R.L. Stevenson, J. Gai. Line segment based image registration. In:Proceedings of the Visual Communications and Image Processing USA: SPIE-The International Society for Optical Engineering,2008:68221-68221
    [133]. Z.N. Li. Stereo correspondence based on line matching in Hough space usingdynamic programming. IEEE Transactions on Systems, Man and Cybernetics.1994,24(1):144-152
    [134]. F. Li, M.K.H. Leung. Hierarchical identification of palmprint using line-basedHough transform. In: Proceedings of the18th International Conference on PatternRecognition, ICPR,2006:149-152

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