双目立体视觉深度感知与三维重建若干问题研究
|
摘要
双目立体视觉是计算机视觉研究领域的重要分支之一,它通过直接模拟人类视觉系统的方式感知客观世界,广泛应用于微操作系统的位姿检测与控制、机器人导航与航测、三维非接触测量及虚拟现实等领域。因此,对双目立体视觉的深度感知与三维重建中的若干问题进行研究具有一定的理论价值和十分重要的现实意义。论文围绕双目立体视觉系统摄像机标定技术、匹配策略与匹配算法、深度信息提取及后续处理、重建方法等重点与难点问题展开研究。论文的主要工作包括以下几个方面。
(1)在对传统摄像机标定方法进行分析的基础上,提出基于棋盘平面方格的半自动摄像机标定方法。该方法以Tsai的两步标定方法为基础,对单个摄像机先进行线性求解,再对该解进行基于最大似然标准的非线性求精；考虑到镜头径向畸变,采用非线性优化方法提高标定精度；最后采用所求的单个摄像机的旋转矩阵以及平移向量进行双目摄像机的标定。该标定方法只需要摄像机拍摄模板平面在两个以上不同方向的图像,模板可以自由的运动,无需其运动的参数。实验结果表明该标定方法灵活,操作简单,且能满足实验精度的要求。
(2)针对区域匹配的速度与精度以及匹配窗口大小难以选择等问题,提出一种基于窗口代价函数的自适应窗口立体匹配算法。为了解决抗噪与视差不连续处误匹配问题,将ρσ(n)函数与四方向线形掩膜技术相结合构造新的相似性测度函数；根据匹配窗口内误差均值、误差方差及大窗口偏移来确定窗口内的像素是否都具有相同视差来构造窗口代价函数,由此窗口代价函数确定匹配窗口尺寸,形成自适应窗口；引入整数图像技术以提高算法的计算效率；为了实现区域匹配全局最优,将遗传算法引入到所设计的自适应窗口匹配算法中。实验结果表明,所提出的匹配算法能较好解决视差不连续区域和遮挡区域的误匹配问题,提高了匹配精度。
(3)为了利用区域匹配视差图的致密性以及特征匹配视差图的鲁棒性,将两种算法结合起来提出了一种基于Harris角点与区域联合的立体匹配算法。针对经典Harris角点检测算子对尺度比较敏感的缺点,提出了一种具有尺度不变特征的Harris角点提取方法,该方法以尺度空间为基础,将空间和尺度两个域中的极值点确定为角点；为了满足不同尺度下提取角点特征的要求,对Harris的图像亮度自相关矩阵M增加了尺度自适应的处理；针对特征匹配得到视差图稀疏问题,结合区域匹配算法以及视差梯度约束,获得致密的视差图像。实验结果表明,所提出的联合的匹配算法可得到致密的视差图,并在边缘等视差不连续区域可获得较好的匹配精度,验证了算法的有效性和可行性。
(4)针对由于匹配误差及标定偏差等原因带来深度图不平滑,深度图的边界与物体边界不对齐等问题,提出一种深度图像结合原始图像的三步联合双边滤波的深度后处理方法,该方法先对原始图像进行双边滤波,再结合原始图像对深度图像进行双边滤波,最后,将两次得到的双边滤波结果再次联合对深度图像进行双边滤波。深度的感知测量与三维重建是立体视觉的最终目标,对基于双目视差的非接触式3D测量与深度提取方法进行了研究与推导,在基于MFC的OpenGL开发平台上,采用局部三维Delaunay的网格构造算法,对标准图像库以及实测数据进行深度提取与三维重建。实验结果表明,所提出的深度后处理方法能有效地实现深度平滑与图像对齐,三维重建较好的直观效果也表明所提出的标定方法、匹配方法以及深度后处理方法的可行性和有效性。
Binocular Stereo vision is an important branch of field of Computer Vision. It simulates the visual system of the human being to perception objective world, which is widely applied into many fields, such as position detection and control of the micro-operation system, robot navigation and aerial survey, three-dimension non-contact measure,virtual reality and so on. So it has important theory value and real significant to study on some issues of depth perception and three dimension reconstruction from binocular stereo vision. Focusing on camera calibration, match strategy and matching algorithm, depth information calculation and three dimension reconstructions based on Open GL, the author had done amount of researches on these aspect. The followings are the main researching content in the paper.
(1) On the basis of analysis of the traditional camera calibration methods, a semi-auto camera calibration method based on a plane plaid was proposed. The calibration method based on Tsai's two steps calibration method calculated the linear solution for a single camera. And then the solution was refined by maximum likelihood standard. Considering the radial distortion of the camera the nonlinear optimization was used to enhance the accuracy of the calibration result. The rotate matrix and translate vector of a single camera was applied into the calibration of the stereo visual system. The proposed calibration method only needed two or more images of the plane plaid from different angles. The plane plaid can move freely without any moving parameters. And the operation of the proposed method was simple but effective. Several experiment results show that the proposed calibration method is feasible and can satisfy the accuracy.
(2) Aiming at matching speed and window size selecting problems of area-based matching an adaptive window size matching algorithm based on cost function of the window was proposed. In order to solve the noise and miss matching at the discontinuous region, a novel similarity measure function was designed using ρσ(n) function combined the four-direction line mask. We used error mean and variance of matching window and big window shift to judge that the disparity values of the window were same or nearly same. According with disparity distribution of the window the cost function was built and was applied to determine the window size adaptively. And integer image technique was introduced into the algorithm for enhance the matching efficient. In order to get global optimal matching for certain region block Genetic algorithm was introduced to the adaptive window matching algorithm. Several experiments results show that the proposed stereo matching algorithm could well solve the disparity discontinuity and occlusion problems. And the accuracy of matching was greatly improved by modified Genetic algorithm that is a global optimum method.
(3) Generally area matching algorithm can get dense disparity. But it couldn't work well at edge. And feature matching algorithm has better robust at feature points matching except that disparity image is sparse. According to the merits and disadvantage of both area matching and feature matching a hybrid matching algorithm combined Harris corner matching and area-based matching was proposed. Considering that classic Harris corner detection algorithm was sensitive to scale changing, an improved Harris feature extraction algorithm was proposed. The improved method was on base of space and scale, which had scale invariance. The pixel at which extremum could be gotten both in the image space and scale space would be considered as a corner feature point. And the self-relative matrix M of image brightness in Harris algorithm was introduced scale adjusting adaptively for extract the corners under different scales. Considering the disparity of feature matching was sparse, we combined with area matching and constraints of disparity gradient. A dense disparity image was gained by the proposed matching scheme. Several experiments show that the proposed hybrid matching algorithm based on area and feature matching could get dense disparity image. And a good matching accuracy at disparity discontinues edge. In a word the proposed algorithm was valid and feasible.
(4) Generally the depth image gotten from the form was unsmooth because of matching error and calibration bias. And some edges of the depth image were not aligned to those of the original image. Aiming at those problems a depth post-processing method was proposed, which was combined the original image and depth image to filter the depth image by three-step joint bilateral filter. The proposed method firstly used bilateral filter on the original image. And then the joint bilateral filter combined with original image was done on depth image. Finally the results of two times filters were joined together and filtered the depth image by bilateral filter again. Depth perception and measure and three dimension reconstruction are aims of stereo vision. We also have done some works on non-contact measure method of three dimensions and deduced the depth computing formula. Finally the grid building algorithm of local3D Delaunay was applied to visualize the three dimensions and depth image by OpenGL combined with MFC developing platform. Some experiments have done on both standard test data and real data. The experiments results show that the proposed post-processing method can smooth the depth image and align the image validly. And several reconstruction results also shows that the proposed calibration methods, the proposed matching method and depth post-processing method are feasible and valid.
(1)在对传统摄像机标定方法进行分析的基础上,提出基于棋盘平面方格的半自动摄像机标定方法。该方法以Tsai的两步标定方法为基础,对单个摄像机先进行线性求解,再对该解进行基于最大似然标准的非线性求精；考虑到镜头径向畸变,采用非线性优化方法提高标定精度；最后采用所求的单个摄像机的旋转矩阵以及平移向量进行双目摄像机的标定。该标定方法只需要摄像机拍摄模板平面在两个以上不同方向的图像,模板可以自由的运动,无需其运动的参数。实验结果表明该标定方法灵活,操作简单,且能满足实验精度的要求。
(2)针对区域匹配的速度与精度以及匹配窗口大小难以选择等问题,提出一种基于窗口代价函数的自适应窗口立体匹配算法。为了解决抗噪与视差不连续处误匹配问题,将ρσ(n)函数与四方向线形掩膜技术相结合构造新的相似性测度函数；根据匹配窗口内误差均值、误差方差及大窗口偏移来确定窗口内的像素是否都具有相同视差来构造窗口代价函数,由此窗口代价函数确定匹配窗口尺寸,形成自适应窗口；引入整数图像技术以提高算法的计算效率；为了实现区域匹配全局最优,将遗传算法引入到所设计的自适应窗口匹配算法中。实验结果表明,所提出的匹配算法能较好解决视差不连续区域和遮挡区域的误匹配问题,提高了匹配精度。
(3)为了利用区域匹配视差图的致密性以及特征匹配视差图的鲁棒性,将两种算法结合起来提出了一种基于Harris角点与区域联合的立体匹配算法。针对经典Harris角点检测算子对尺度比较敏感的缺点,提出了一种具有尺度不变特征的Harris角点提取方法,该方法以尺度空间为基础,将空间和尺度两个域中的极值点确定为角点；为了满足不同尺度下提取角点特征的要求,对Harris的图像亮度自相关矩阵M增加了尺度自适应的处理；针对特征匹配得到视差图稀疏问题,结合区域匹配算法以及视差梯度约束,获得致密的视差图像。实验结果表明,所提出的联合的匹配算法可得到致密的视差图,并在边缘等视差不连续区域可获得较好的匹配精度,验证了算法的有效性和可行性。
(4)针对由于匹配误差及标定偏差等原因带来深度图不平滑,深度图的边界与物体边界不对齐等问题,提出一种深度图像结合原始图像的三步联合双边滤波的深度后处理方法,该方法先对原始图像进行双边滤波,再结合原始图像对深度图像进行双边滤波,最后,将两次得到的双边滤波结果再次联合对深度图像进行双边滤波。深度的感知测量与三维重建是立体视觉的最终目标,对基于双目视差的非接触式3D测量与深度提取方法进行了研究与推导,在基于MFC的OpenGL开发平台上,采用局部三维Delaunay的网格构造算法,对标准图像库以及实测数据进行深度提取与三维重建。实验结果表明,所提出的深度后处理方法能有效地实现深度平滑与图像对齐,三维重建较好的直观效果也表明所提出的标定方法、匹配方法以及深度后处理方法的可行性和有效性。
Binocular Stereo vision is an important branch of field of Computer Vision. It simulates the visual system of the human being to perception objective world, which is widely applied into many fields, such as position detection and control of the micro-operation system, robot navigation and aerial survey, three-dimension non-contact measure,virtual reality and so on. So it has important theory value and real significant to study on some issues of depth perception and three dimension reconstruction from binocular stereo vision. Focusing on camera calibration, match strategy and matching algorithm, depth information calculation and three dimension reconstructions based on Open GL, the author had done amount of researches on these aspect. The followings are the main researching content in the paper.
(1) On the basis of analysis of the traditional camera calibration methods, a semi-auto camera calibration method based on a plane plaid was proposed. The calibration method based on Tsai's two steps calibration method calculated the linear solution for a single camera. And then the solution was refined by maximum likelihood standard. Considering the radial distortion of the camera the nonlinear optimization was used to enhance the accuracy of the calibration result. The rotate matrix and translate vector of a single camera was applied into the calibration of the stereo visual system. The proposed calibration method only needed two or more images of the plane plaid from different angles. The plane plaid can move freely without any moving parameters. And the operation of the proposed method was simple but effective. Several experiment results show that the proposed calibration method is feasible and can satisfy the accuracy.
(2) Aiming at matching speed and window size selecting problems of area-based matching an adaptive window size matching algorithm based on cost function of the window was proposed. In order to solve the noise and miss matching at the discontinuous region, a novel similarity measure function was designed using ρσ(n) function combined the four-direction line mask. We used error mean and variance of matching window and big window shift to judge that the disparity values of the window were same or nearly same. According with disparity distribution of the window the cost function was built and was applied to determine the window size adaptively. And integer image technique was introduced into the algorithm for enhance the matching efficient. In order to get global optimal matching for certain region block Genetic algorithm was introduced to the adaptive window matching algorithm. Several experiments results show that the proposed stereo matching algorithm could well solve the disparity discontinuity and occlusion problems. And the accuracy of matching was greatly improved by modified Genetic algorithm that is a global optimum method.
(3) Generally area matching algorithm can get dense disparity. But it couldn't work well at edge. And feature matching algorithm has better robust at feature points matching except that disparity image is sparse. According to the merits and disadvantage of both area matching and feature matching a hybrid matching algorithm combined Harris corner matching and area-based matching was proposed. Considering that classic Harris corner detection algorithm was sensitive to scale changing, an improved Harris feature extraction algorithm was proposed. The improved method was on base of space and scale, which had scale invariance. The pixel at which extremum could be gotten both in the image space and scale space would be considered as a corner feature point. And the self-relative matrix M of image brightness in Harris algorithm was introduced scale adjusting adaptively for extract the corners under different scales. Considering the disparity of feature matching was sparse, we combined with area matching and constraints of disparity gradient. A dense disparity image was gained by the proposed matching scheme. Several experiments show that the proposed hybrid matching algorithm based on area and feature matching could get dense disparity image. And a good matching accuracy at disparity discontinues edge. In a word the proposed algorithm was valid and feasible.
(4) Generally the depth image gotten from the form was unsmooth because of matching error and calibration bias. And some edges of the depth image were not aligned to those of the original image. Aiming at those problems a depth post-processing method was proposed, which was combined the original image and depth image to filter the depth image by three-step joint bilateral filter. The proposed method firstly used bilateral filter on the original image. And then the joint bilateral filter combined with original image was done on depth image. Finally the results of two times filters were joined together and filtered the depth image by bilateral filter again. Depth perception and measure and three dimension reconstruction are aims of stereo vision. We also have done some works on non-contact measure method of three dimensions and deduced the depth computing formula. Finally the grid building algorithm of local3D Delaunay was applied to visualize the three dimensions and depth image by OpenGL combined with MFC developing platform. Some experiments have done on both standard test data and real data. The experiments results show that the proposed post-processing method can smooth the depth image and align the image validly. And several reconstruction results also shows that the proposed calibration methods, the proposed matching method and depth post-processing method are feasible and valid.
引文
[1]马颂德,张正友.计算机视觉一计算机理论与算法基础[M].北京：科学出版社,1998.
[2]Roberts, L, Machine perception of three-dimensional solids, In J. Tippetl Eds. Optical and Electro-optical information processing, Cambridge, Mass, MIT Press, 1965:159-197.
[3]Marr D.Image processing and machine vision [M].USA:Freeman and compay,1973.
[4]Marr D.Vision. San Francisco:W. H. Freeman and Company,1982.
[5]Negahdaripour,S & Jain, A.,K.Final Report of the NSF Workshop on the Challenges in Computer Vision Research, Future Directions of Research, Lahina, Maui, Hawaii,Jane 78,1991.
[6]章毓晋.图象工程(下册)-图象理解与计算机视觉[M].北京：清华大学出版社，2000.
[7]贾云得.机器视觉[M].北京：科学出版社,2000.
[8]何欣年.航空数字相机的发展与应用[J].遥感技术与应用,2000,15(2)：124-129.
[9]Boyde A. Photogrammetry of stereo pair SEM image using separate measurement from the two images. SEM/I, Illinois Institute of Technology Research,1974: 101-110.
[10]邱茂林,马颂德,李毅.计算机视觉中摄像机定标综述[J].自动化学报，,2000,26(1)：43-55.
[11]Faig W. Calibration of close-range photogrammetric systems:mathematical formulation.photogrammetric eng.Remote sensing,1975.41(12):1479-1486.
[12]Dainis A, Juberts M. Accurate remote measurement of robot trajectory motion. In:Proc. Int. Conference onRobotics and Automation.1985.92-99.
[13]Ganapathy S. Decomposition of transformation matrices for robot vision. In:Proc. Int. Conference on Roboticsand Automation.1984.130-139.
[14]Martins H A, Birk J R, Kelley R B. Camera models based no data from two calibration planes.ComputerGraphics and Imaging Processing.1981,17:173-180.
[15]游素亚,徐光祐.立体视觉研究的现状与进展[J].中国图象图形学报,1997,2(1)：17-23
[16].Dondes PA, Rosenfeld A. Fixed classification based ongray level and local "busyness". IEEE PMAI,1982,4(1):79.
[17]De Souza P.Edge based segmentation and texture sepa-ration, Proceedings, 5thlnt. Con. on Pattern Recogn-ition,1980,554.
[18]Venkateswar V, Chellappa R. Hierarchical stereo and motion correspondence using feature groupings[J]. Int Jof Computer Vision,1995,15(3):245-269.
[19]Birchfield S, Tomasi C. Multiway cut for stereo andmotion with slanted surfaces[C]. Proc of Int Conf onComputer Vision. Greece,1999:489-495.
[20]Kolmogorov V, Zabin R. What energy functions can beminimized via graph cuts? [J]. IEEE Trans on PatternAnalysis and Machine Intelligence,2004,26(2): 147-159.
[21]Kim H, Sohn K. Hierarchical disparity estimation withenergy-based regularization[C]. Proc of Int Conf onImage Processing. Barcelona,2003: 373-376.
[22]Behrooz K P. Minimizataion of the quantization error in camera calibration, proc. of image Understand work shop,1987:671-680.
[23]Blostein S D. et al. Error analysis in stereo determination of 3-D point positions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987,9:752-765.
[24]Gem-Sun J Young.3D motion estimation using a sequence of noisy image:Models Estimation and Uniqueness Result[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12 (8).
[25]Grimson W E L. Surface Consistency constrsints in Vision [J]. Computer Vision,Graphics,and Image Processing,1983,24:28-51.
[26]Grimson W E L. A computation theory of visual surface interpolation[J]. Philosophical Transactions of the Royal Society of London,1982,B298:395-427.
[27]Terzopoulos D.The Computation of Visible-Surface Representations[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1988,10 (4) 417-438.
[28]Maitre H, et al. Using Models to Improve Stereo Reconstruction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,149(2): 269-277.
[29]Daniel Scharstein, Richard Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms[J].International Journal of Computer Vision,2002,47(1/2/3):7-42.
[30]Boykov Y, Kolmogorov V. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(9):1124-1137.
[31]J.Sun,N.N.Zheng,and H.Y.Shum.Stereo Matching using Belief Propagationg[J].IEEE Transactions on Pattern Analysis and Machine Intelligence.2003,25(7):787-800.
[32]Bleyer M,Gelautz M.A layered stereo matching algorithm using image segmentation and global visibility constraints[J].ISPRS Journal of Photogrammetry and Remote Sensing,2005,59(3):128-150.
[33]Bleyer M,Gelautz M.Graph-cut-based stereo matching using image segmentation with symmetrical treatment of occlusions[J]. Signal Processing Image Communication:2007,22(2):127-143.
[34]Liang Wang, et al. High-quality Real-time Stereo using Adaptive Cost Aggregation and Dynamic Programming[C].Proceedings of the Third International Symposium on 3D Data Processing Visualization and Transmission.Washington, DC, USA:IEEE Computer Society,2006:798-805.
[35]尹传历,刘冬梅,宋建中.改进的基于图像分割的立体匹配算法[J].计算机辅助设计与图形学学报.2008，20(6)：808-512.
[36]徐青等.基于图像分割的快速立体匹配算法[J].计算机工程.200832(22)：209-211.
[37]彭启民,贾云得.一种基于最小割的稠密视差图恢复算法[J].软件学报.2005,16(6)：1090-1095.
[38]周秀芝,王润生.基于像元集的置信传递立体匹配[J].中国图象图形学报.2008,13(3)：506-512.
[39]Joseph.H. Intersection mappings for stereo vision, SPIE,1991,1608:293-299.
[40]Y Shirai.Three Dimensional Computer Vision-Symbolic Computation[M], Spring-Verlag, Berlin,1987.
[41]B.F.Buxton, D.A.Castelow, M.J.Dell. Evaluation of a stereo vision system for the control of an autonomous guided vehicle. SPIE,1993,1989:104-115.
[42]张健新,段发阶,钟明等.用于三维尺寸检测的双目视觉传感器[J].计量学报,1999,20(2)：96-99.
[43]D. Marr,Vision, W. H. Freeman and Company, San Francisco.中译本：视觉计算理论[M],姚国正,刘磊,汪云九译,科学出版社,1988.
[44]S.De Paoli,R.Zigmann,T.Skordas,H.Huot Soudain.Evaluation of an Obstacle Detection Technique Based in Stereo-Vision. SPIE,1993,1989:128-136.
[45]Kei,Masayuki.Integration of real-time binocular stereo vision and whole bodyinformation for dynamic walking navigation of humanoid robot.IEEE Conference on Multisensor Fusion and Integration for Intelligence ystems, Tokyo, July 2003:131-36.
[46]Minoru Asada,Takamaro Tanaka.Visual Tracking Of Unknown Moving Object By Adaptive Binocular Visual Servoing.Porceeding of the 1999 IEEE Intenraitonal Confeernce on Multisensor Fusion and Inteligent Systems.
[47]Vallcrand Stcvc,Kanbara Masaynki,Yokoya Naokazn.Binoc nlar Vision-Based Augmented Reality System With An In-creased Registration Depth Using Dynamic Correction Of Fcatore Positions. Procecdings.Of the 2003 IEEE, Virtnal RcaLity March 2003;Vol.22-26,p271-272.
[48]Yajnn Fang, Ichiro Masaki, and Bcrthold Horn.Depth-Based Target Segmentation for Intelligent Vehicles:Fusion of Radar and Binocular Stereo.IEEE Transactions On Intelligent Transportation Systems,2002;3(3).
[49]Clark F. Olson, Habib Abi-Rachcd, Ming Ye,Jonathan P.Hendrich.Wide-Baseline Stereo Vision For Mars Rovers. Proceedings Of The 2003 IEEE/RSJ Intl. Conference On In-tclligent Robots And Systenns. October 2003.
[50]A.Bensrhair, M.Bertozzi, et al.Stereo Vision-based Feature Extraction for Vehicle Detection. IEEE Intelligent Vehicles Symp 2002.
[51]隋婧,金伟其,阮玉峰.双目立体视觉技术的实现及其进展[J].电子技术应用,2004；(10)：5,6,12.
[52]阮晓东,李世伦,诸葛良等.用立体视觉测量多自由度机械装置姿态的研究[J].中国机械工程,2000,11(5)：571-573.
[53]高庆吉,洪炳熔,阮玉峰.基于异构双目视觉的全自主足球机器人导航[J].哈尔滨工业大学学报,2003；35(9。
[54]管业鹏,童林夙,陈娜.基于双目立体视觉的偏转线圈测量方法研究[J].电子学报,2003,31(9)：1382-1385.
[55]Faugeras O,Toscani G.The calibration problem for stereo. In:Proc CVPR'86, Miami Beach, FL,1986,15-20.
[56]Wei G Q,Ma S D.Camera calibration by vanishing point and cross ratio.In:Proc IEEE Int Conf ASSP'89, Glasgow Scotland,1989.1630-1633.
[57]高立志,方勇,林志航等.高精度立体视觉测量中一种通用的摄像机标定技术[J].机械科学与技术,1998,17(5)：808-811.
[58]Ma S D. A self-calibration technique for active vision system. IEEE Trans on Robot Automat,1996,12(1):114-120.
[59]吴立德著.计算机视觉[M].上海：复旦大学出版社,1993.
[60]J.Weng,P.Cohen and M.Herniou.Camera calibration with distortion models and accuracy evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1992,14(10):965-980.
[61]R.Y. Tsai.An efficient and accurate camera calibration technique for 3D machine vision.Proc.of IEEE Conference of Computer Vision and Pattern Recognition 1986.
[62]G. Q.Wei and S. D.Ma.Implicit and explicit camera calibration:theory and experiment. IEEE Trans. Pattern Recognition and Machine Intelligence, 1994,16(5).
[63]Abdel-Aziz Y I,Karara H.M.Direct linear transformation from compcoordinates into object space coordinates in close-range photogrammetry. In Proceedings Symposium on Close-Range Photogrammetry,1971,1-18.
[64]Jerry Hobbs,Doug Appelto.FASTUS:a system for extracting information from natural language text[R]. Technical Report 519, SRI,1992.
[65]Linda G. Shapiro,George C. Stockman. Computer Vision[M].北京:机械工业出 版社,327-333,2005.
[66]马颂德,张正友.计算机视觉-计算理论与算法基础.北京：科学出版社,2003.
[67]Zhang Z.Flexible camera calibration by viewing a plane from unknown orientations[C].ICCV,1999:666-673.
[68]LuoGuie,Yang Xinrong.FAST AND ACCURATE CAMERA CALIBRATION METHOD BASED ON GRID-BASED BOARD PLAND. ELECTRONIC INFORMATION SCIENCE AND TECHNOLOGY.
[69]Q.-T.Luong and O.Faugeras.Self-calibration of a moving camera from point correspondences and fundamental matrices. The International Journal of Computer Vision,1997,22 (3):261-289.
[70]J. More.The levenberg-marquardt algorithm, implementation and theory.In G. A. Watson, editor, Numerical Analysis, Lecture Notes in Mathematics 630. Springer-Verlag,1977.
[71]姜大志,郁倩等.计算机视觉成象的非线性畸变研究与综述[J].计算机工程,2001,27(12)：108-110.
[72]陈胜勇,刘盛等.基于OpenCV的计算机视觉技术实现[M].北京：科学出版社, 2011：370-371.
[73]Marr D,et al. Cooperative computation of stereo disparity.Sci.1976194:283-287.
[74]Poggio T,et al.Computational vision and regularization theoery.Nature,1985, 317:314-319.
[75]C.L.Zitnick, T.Kanade, A cooperative algorithm for stereo matching and occlusion detection, Proceedings IEEE Transactions Pattern Analysis and Machine Intelligence,2000,22(7):675-684.
[76]李德广,李科杰,高丽丽.基于多尺度多方向相位匹配的立体视觉方法[J].仪器仪表学报,2004,25(4)增刊：600-602.
[77]罗桂娥,徐运彬.基于Quaternion-Gabor小波运动估计得深度图获取技术[J].计算机应用,2011.
[78]Jenkin M, et al. The measurement of binocular disparity in computational process in human vision [M]. Ablex. NewJersey:Prentice-Hall,1988.
[79]Muquit M A, Shibahara T, Aoki T. A high-accuracy passive 3D measurement system using phase-based image matching [J]. IEICE Trans on Fundamentals of Electronics, Communications and Computer Sciences,2006, E89-A(3):686-697.
[80]游素亚,柳健,万发贯.基于小波变换相位的立体匹配系统[J].信息与控制,1994,(5)：28-31.
[81]徐奕,周军,周源华.基于小波及动态规划的相位匹配[J].上海交通大学学报,2003，37(3)：388-392.
[82]Han.J.H, Park.J.S, Contour matching using epipolar geometry, IEEE Transactions on Pattern Analysis and Machine Intelligence 2000,22(4):358-370.
[83]邓志燕,陈炽坤.利用外极线约束的图像匹配新算法[J].工程图学学报,2009,5(8)：104-107.
[84]顾征,苏显渝.采用色彩相似性约束的立体匹配[J].光电工程,2007,34(1)：95-99.
[85]J.P.Semple,L.Roth. Introduction to Algebraic Geometry,Clarendon Press, Oxford,1949.
[86]Longuet-Higgins,H.C.A computer algorithm forreconstruction a scene from two projections. Nature,1981,293:133135.
[87]R.Zabih,J.Woodfill.Non-parametric local transforms for computing visual correspondence,3rd European Conference on Computer Vision,1994:151-158.
[88]白明,庄严,王伟.双目立体匹配算法的研究与进展[J].控制与决策,2008,23(7)：721-729.
[89]Scliarstein D, Szeliski R.A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algoritlun [J].International Journal of Computer Vision (S0920-5691),2002,47(1):7-42.
[90]Bobick A F, Sintille S.Large Occlusion Stereo [J].International Journal of Computer Vision (S0920-5691),1999,33(3),181-200.
[91]Lenng S C,Appleton B,Sun C.Fast Stereo Matching by Iterated Dynamic Programmurg and Quadtree Snbregioning [C.].Proceedings of the Conference on British Machine Vision. London:BMVA Press,2004:97-106.
[92]Bircbfield S,Tomasi C.Depth Discontumities by Pixel-to-Pixel Stereo [C].Proceedurgs of International Conference on Computer Vision, Bombay USA: IEEE Press,1998:1073-1080.
[93]卢阿丽,唐振民,杨静宇.一种基于双向动态规划的立体匹配算法[J].系统仿真学报,2008,2(13)：3378-3380,3384.
[94]Zhang Z,Deriche R,Faugerrau O et al.A mhust technique for matching two unccalibrated images through the recovey of the unknown epipolar geometry [J]. A rtificial Intelligence 1995,78(1-2):87-119.
[95]Zhang W,Gao X,Sung E et al.A feature-based matching scheme M PCD and robust matching strategy [J]. Pattern Recognition Letters 2007,28 (10) 1222-1231.
[96]张辉,张丽艳,陈鉴富等.基于自适应迭代松弛的立体点对匹配鲁棒算法[J].中国图象图形学报,2009,14(7)：1378-1385.
[97]Bleyer M.Gelautz M.A layered stereo algorithm using image segment-ation and global visibility constraints[J].ISPRS Journal of Photogra-mmetry and Remote Sensing,2005,59(3):128-150.
[98]Bleyer M.Gelautz M.Graph-cut-based stereo matching using image Segme-ntation Wilth Symmetrical treatment of occlusions[J].Signal Processing:Image Communication,2007,22(2):127-143.
[99]徐彦君,杜利民,侯自强等.一种用于立体匹配的改进的神经网络方法[J].中国图象图形学报,1998,3(10)：845-848.
[100]陈国良,王煦法,庄镇泉.等遗传算法及其应用[M].北京：人民邮电出版社,1996.
[101]陈宁江,李介谷,邓雁萍.遗传算法在立体图象匹配中的应用[J].上海交通大学学报,1998.32(9)：49-52.
[102]罗桂娥,杨欣荣.黄维.基于分块变换-融合滤波的改进EZW算法[J].计算机工程,2009,35(6)：225-227.
[103]杨玉娟,都思丹.分层正交动态规划立体匹配算法[J].中国科学技术大学学报,2009,39(11)：1171-1176.
[104]Gyung-Bum Kim,Sung-Chong.An accurate and robust stereo matching algorithm with variable windows for 3D measurements..Mechatronics,2004,14, 715-735.
[105]Kanade T, Okutomi M. A stereo matching algorithm with an adaptive window:Theory and experiment. IEEE Transactions on Pattern Analysis and Machine Intelligence,1994,16:920-932.
[106]Veksler O. Fast variable window for stereo correspondence using integral images.In:Processdings for the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA,2003,556-561.
[107]LuoGuie,Yang Xinrong,Xu Qi. Fast stereo matching algorithm using adaptive window. ISIP 2008 and WMWA 2008:25-30.
[108]许奇,罗桂娥,刘献如.基于整数图像技术的自适应窗口立体匹配算法[J].计算机仿真,2007,24(11)：218-220.
[109]陈国良,王煦法等.遗传算法及其应用[M].北京：人民邮电出版社,2006.
[110]吕朝辉,张兆扬,安平.一种基于遗传算法的立体匹配方法[J].计算机工程2003,29(20)：24-25,30.
[111]陈宁江.李介谷.邓雁萍.遗传算法在立体图象匹配中的应用[J].上海交通大学学报,1998,32(9)：49-52.
[112]王彪,王金岩,何亦征,沈春林.一种立体匹配的新算法[J].计算机工程,2005,.31(10)：153-155.
[113]汪剑鸣,许镇琳.浮点遗传算法中一种新的杂交算子[J].控制理论与应用,2002,19(6)：977-980.
[114]Davis L.Handbook of Genetic Algorithms. New York:Van Nostrand Reinhold, 1991.
[115]Fogel D B.An Introduction to Simulated Evolutionary Optimization. IEEE Transactions on Neural Networks,1994,5(1):3-14.
[116]梁俊明.遗传算法研究：[硕士学位论文].西安：西安电子科技大学,2002.
[117]Ming-Lun Gong, Yee-Hong Yang. Genetic-based stereo algorithm and disparity map evaluation. International Journal of Computer Vision,2002,47(1/2/3),63-77.
[118]周东翔,蔡宣平,孙茂印.一种基于特征约束的立体匹配算法[J].中国图像图形学报,2001,6(7)：653-656.
[119]贾波,章毓晋,林行刚.视差图误差检测与校正的通用快速算法[J].清华大学 学报(自然科学版),2000,40(1)：28-31.
[120]Lindeberg T.Feature detection with automatic scale selection[J].International Journal of Computer Vision,1998,30(2):79-116.
[121]Lindeberg T. Edge detection and ridge detection with automatic scale selection [J]. International Journal of Computer Vision,1998,30(2):117-156.
[122]Mikolajczyk K,Schmid C.Scale and affine invariant interestpoint detectors[J].International Journal of Computer Vision,2004,60(1)63-66.
[123]程邦胜,唐孝威Harris尺不变性关键点检测子的研究[J].浙江大学学报：工学版,2009,43(5)：855-859.
[124]Harris C, Stevens M.A combined comer and edge detector [C].Proceedings of the 4th Alvey Vision Conference,UK:the Plessey Company,1988:147-151.
[125]A.Fusiello, V.Roberto, Efficient stereo with multiple windowing, IEEE Conference on Computer Vision and Pattern Recognition,1997,858-863.
[126]H.Hirschmuller,Improvements in real-time correlation-based stereo vision, Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision,2001.
[127]黄帅,吴克伟,苏菱.基于H arris尺度不变特征的图像匹配方法[J].合肥工业大学学报(自然科学版),2011,34(3).
[128]Burt P,Julesz B.Modifications of the classical notion of Panum's fusional area[J].Percept,1980,9:671-682.
[129]Payman Moallem,Karim Faez.Search Space Reduction in the Edge Based Stereo Correspondence[J].Stuttg,2001,11:21-23.
[130]LuoGuie;Yang Xinrong; Liu, Wei.A Fast Azd Robust Based on Stero Matching Algorithm.TECHNOLOGY AND APPLICATION OF ELECTRONIC INFORMATION,2009:25-55.
[131]刘维,罗桂娥,杨欣荣.一种快速鲁棒区域匹配算法[J].微计算机信息,2009,25(11)：184-185，191.
[132]Z.Li,G. Hu.Analysis of Disparity Gradient Based Cooperative Stereo[J].IEEE Trans. On Image Procesing,1996,5:1493-1506.
[133]罗桂娥,刘小群.区域与特征相结合的立体匹配算法[J].计算机安全,2011(5)：14-17.
[134]邱阳,白立芬,李庆祥,徐毓娴.基于边缘和区域联合匹配的立体视觉新方法[J].仪器仪表学报,：2001,21(3)：255-256.
[135]高一宁,韩燮.基于Canny算子和Harris角点提取的三维重建方法[J].电子测试,2011(2)：11-16.
[136]Rui Li,Stan Sclaroff.Multi-scale 3D scene flow from binocular stereo sequences[J].Computer Vision and Image Understanding,2008(110):75-90.
[137]李海军,王洪丰,沙焕滨.平行双目视觉系统的三维重建研究[J].自动化技术与应用,2007,26(6)：56-58.
[138]郑竺英.双目立体视觉的信息加工[M].北京：科学出版社,1998.
[139]袁智,罗桂娥.基于模糊推理的方向中值椒盐滤波算法[J].微型机与应用,2010(6)：28-30.
[140]Tomasi C,Manduchi R.Biltateral filtering for gray and color images[A]. Proceedings of the 1998 IEEE International Conference on Computer Vision[C], Washington D C:IEEE Computer Society,1998.839-846.
[141]张志强,王万玉.一种改进的双边滤波算法[J].中国图象图形学报,2009,14(3)：443-447.
[142]靳明,宋建中.一种自适应的图像双边滤波方法[J].光电工程,2004,31(7)：65-67.
[143]王琼华.3D显示技术与器件[M].北京：科学出版社,2011.
[144]杨中东,雷玉堂.单CCD摄像机的一种三维自动测量系统[J].光电工程,2007,34(3),127-131.
[145]Taguchi A. Miyoshi T. Takaya Y. et al.3Dmicro-profile measurement using optical inverse scattering phase method[J]. Ann CIRP,2000,49(1):423-429.
[146]刘浩然,张文明,刘彬.基于双目立体视觉实现物体三维测量的研究[J].光子学报,2009,38(7)：1830-1834.
[147]Dave Shreiner.李军,徐波译OpenGL编程指南(第六版)[M].北京：机械工业出版社,2010.
[148]Bernardini F.Miuleman J.Rushmeier H.eL al. The Ball-Pivoting Algorithm for Surface ReconsLrucLion[J].IEEE Transactions on Visualization and Computer Graphics 1999,5 (4):349-359.
[149]Chaine R. A geometric convection approach of 3-D reconslruction[C]//E-urographics Symposium on Geometry Processing.[s.l.]:[s.n.],2003
[150]田金文,柳健,彭复员,张天序.三维可视表面重建中的内插算法[J].华中理工大学学报,1995,23(9)：24-27.
[151]Choi B K, Shin H Y, Yoon Y I et al.Triangulation of scattered data in 3D space[J]. CAD,1988,20(5):239-248.
[152]石琼,沈春林,谭皓.基于OpenGL的三维建模实现方法[J].计算机工程与应用,2004(18)122-124.
[153]高宏伟,于洋,刘晓阳.双目立体视觉三维重建实验平台研究[J].计算机工程与应用,2009,45(33)：149-152.
[2]Roberts, L, Machine perception of three-dimensional solids, In J. Tippetl Eds. Optical and Electro-optical information processing, Cambridge, Mass, MIT Press, 1965:159-197.
[3]Marr D.Image processing and machine vision [M].USA:Freeman and compay,1973.
[4]Marr D.Vision. San Francisco:W. H. Freeman and Company,1982.
[5]Negahdaripour,S & Jain, A.,K.Final Report of the NSF Workshop on the Challenges in Computer Vision Research, Future Directions of Research, Lahina, Maui, Hawaii,Jane 78,1991.
[6]章毓晋.图象工程(下册)-图象理解与计算机视觉[M].北京：清华大学出版社，2000.
[7]贾云得.机器视觉[M].北京：科学出版社,2000.
[8]何欣年.航空数字相机的发展与应用[J].遥感技术与应用,2000,15(2)：124-129.
[9]Boyde A. Photogrammetry of stereo pair SEM image using separate measurement from the two images. SEM/I, Illinois Institute of Technology Research,1974: 101-110.
[10]邱茂林,马颂德,李毅.计算机视觉中摄像机定标综述[J].自动化学报，,2000,26(1)：43-55.
[11]Faig W. Calibration of close-range photogrammetric systems:mathematical formulation.photogrammetric eng.Remote sensing,1975.41(12):1479-1486.
[12]Dainis A, Juberts M. Accurate remote measurement of robot trajectory motion. In:Proc. Int. Conference onRobotics and Automation.1985.92-99.
[13]Ganapathy S. Decomposition of transformation matrices for robot vision. In:Proc. Int. Conference on Roboticsand Automation.1984.130-139.
[14]Martins H A, Birk J R, Kelley R B. Camera models based no data from two calibration planes.ComputerGraphics and Imaging Processing.1981,17:173-180.
[15]游素亚,徐光祐.立体视觉研究的现状与进展[J].中国图象图形学报,1997,2(1)：17-23
[16].Dondes PA, Rosenfeld A. Fixed classification based ongray level and local "busyness". IEEE PMAI,1982,4(1):79.
[17]De Souza P.Edge based segmentation and texture sepa-ration, Proceedings, 5thlnt. Con. on Pattern Recogn-ition,1980,554.
[18]Venkateswar V, Chellappa R. Hierarchical stereo and motion correspondence using feature groupings[J]. Int Jof Computer Vision,1995,15(3):245-269.
[19]Birchfield S, Tomasi C. Multiway cut for stereo andmotion with slanted surfaces[C]. Proc of Int Conf onComputer Vision. Greece,1999:489-495.
[20]Kolmogorov V, Zabin R. What energy functions can beminimized via graph cuts? [J]. IEEE Trans on PatternAnalysis and Machine Intelligence,2004,26(2): 147-159.
[21]Kim H, Sohn K. Hierarchical disparity estimation withenergy-based regularization[C]. Proc of Int Conf onImage Processing. Barcelona,2003: 373-376.
[22]Behrooz K P. Minimizataion of the quantization error in camera calibration, proc. of image Understand work shop,1987:671-680.
[23]Blostein S D. et al. Error analysis in stereo determination of 3-D point positions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987,9:752-765.
[24]Gem-Sun J Young.3D motion estimation using a sequence of noisy image:Models Estimation and Uniqueness Result[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12 (8).
[25]Grimson W E L. Surface Consistency constrsints in Vision [J]. Computer Vision,Graphics,and Image Processing,1983,24:28-51.
[26]Grimson W E L. A computation theory of visual surface interpolation[J]. Philosophical Transactions of the Royal Society of London,1982,B298:395-427.
[27]Terzopoulos D.The Computation of Visible-Surface Representations[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1988,10 (4) 417-438.
[28]Maitre H, et al. Using Models to Improve Stereo Reconstruction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,149(2): 269-277.
[29]Daniel Scharstein, Richard Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms[J].International Journal of Computer Vision,2002,47(1/2/3):7-42.
[30]Boykov Y, Kolmogorov V. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(9):1124-1137.
[31]J.Sun,N.N.Zheng,and H.Y.Shum.Stereo Matching using Belief Propagationg[J].IEEE Transactions on Pattern Analysis and Machine Intelligence.2003,25(7):787-800.
[32]Bleyer M,Gelautz M.A layered stereo matching algorithm using image segmentation and global visibility constraints[J].ISPRS Journal of Photogrammetry and Remote Sensing,2005,59(3):128-150.
[33]Bleyer M,Gelautz M.Graph-cut-based stereo matching using image segmentation with symmetrical treatment of occlusions[J]. Signal Processing Image Communication:2007,22(2):127-143.
[34]Liang Wang, et al. High-quality Real-time Stereo using Adaptive Cost Aggregation and Dynamic Programming[C].Proceedings of the Third International Symposium on 3D Data Processing Visualization and Transmission.Washington, DC, USA:IEEE Computer Society,2006:798-805.
[35]尹传历,刘冬梅,宋建中.改进的基于图像分割的立体匹配算法[J].计算机辅助设计与图形学学报.2008，20(6)：808-512.
[36]徐青等.基于图像分割的快速立体匹配算法[J].计算机工程.200832(22)：209-211.
[37]彭启民,贾云得.一种基于最小割的稠密视差图恢复算法[J].软件学报.2005,16(6)：1090-1095.
[38]周秀芝,王润生.基于像元集的置信传递立体匹配[J].中国图象图形学报.2008,13(3)：506-512.
[39]Joseph.H. Intersection mappings for stereo vision, SPIE,1991,1608:293-299.
[40]Y Shirai.Three Dimensional Computer Vision-Symbolic Computation[M], Spring-Verlag, Berlin,1987.
[41]B.F.Buxton, D.A.Castelow, M.J.Dell. Evaluation of a stereo vision system for the control of an autonomous guided vehicle. SPIE,1993,1989:104-115.
[42]张健新,段发阶,钟明等.用于三维尺寸检测的双目视觉传感器[J].计量学报,1999,20(2)：96-99.
[43]D. Marr,Vision, W. H. Freeman and Company, San Francisco.中译本：视觉计算理论[M],姚国正,刘磊,汪云九译,科学出版社,1988.
[44]S.De Paoli,R.Zigmann,T.Skordas,H.Huot Soudain.Evaluation of an Obstacle Detection Technique Based in Stereo-Vision. SPIE,1993,1989:128-136.
[45]Kei,Masayuki.Integration of real-time binocular stereo vision and whole bodyinformation for dynamic walking navigation of humanoid robot.IEEE Conference on Multisensor Fusion and Integration for Intelligence ystems, Tokyo, July 2003:131-36.
[46]Minoru Asada,Takamaro Tanaka.Visual Tracking Of Unknown Moving Object By Adaptive Binocular Visual Servoing.Porceeding of the 1999 IEEE Intenraitonal Confeernce on Multisensor Fusion and Inteligent Systems.
[47]Vallcrand Stcvc,Kanbara Masaynki,Yokoya Naokazn.Binoc nlar Vision-Based Augmented Reality System With An In-creased Registration Depth Using Dynamic Correction Of Fcatore Positions. Procecdings.Of the 2003 IEEE, Virtnal RcaLity March 2003;Vol.22-26,p271-272.
[48]Yajnn Fang, Ichiro Masaki, and Bcrthold Horn.Depth-Based Target Segmentation for Intelligent Vehicles:Fusion of Radar and Binocular Stereo.IEEE Transactions On Intelligent Transportation Systems,2002;3(3).
[49]Clark F. Olson, Habib Abi-Rachcd, Ming Ye,Jonathan P.Hendrich.Wide-Baseline Stereo Vision For Mars Rovers. Proceedings Of The 2003 IEEE/RSJ Intl. Conference On In-tclligent Robots And Systenns. October 2003.
[50]A.Bensrhair, M.Bertozzi, et al.Stereo Vision-based Feature Extraction for Vehicle Detection. IEEE Intelligent Vehicles Symp 2002.
[51]隋婧,金伟其,阮玉峰.双目立体视觉技术的实现及其进展[J].电子技术应用,2004；(10)：5,6,12.
[52]阮晓东,李世伦,诸葛良等.用立体视觉测量多自由度机械装置姿态的研究[J].中国机械工程,2000,11(5)：571-573.
[53]高庆吉,洪炳熔,阮玉峰.基于异构双目视觉的全自主足球机器人导航[J].哈尔滨工业大学学报,2003；35(9。
[54]管业鹏,童林夙,陈娜.基于双目立体视觉的偏转线圈测量方法研究[J].电子学报,2003,31(9)：1382-1385.
[55]Faugeras O,Toscani G.The calibration problem for stereo. In:Proc CVPR'86, Miami Beach, FL,1986,15-20.
[56]Wei G Q,Ma S D.Camera calibration by vanishing point and cross ratio.In:Proc IEEE Int Conf ASSP'89, Glasgow Scotland,1989.1630-1633.
[57]高立志,方勇,林志航等.高精度立体视觉测量中一种通用的摄像机标定技术[J].机械科学与技术,1998,17(5)：808-811.
[58]Ma S D. A self-calibration technique for active vision system. IEEE Trans on Robot Automat,1996,12(1):114-120.
[59]吴立德著.计算机视觉[M].上海：复旦大学出版社,1993.
[60]J.Weng,P.Cohen and M.Herniou.Camera calibration with distortion models and accuracy evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1992,14(10):965-980.
[61]R.Y. Tsai.An efficient and accurate camera calibration technique for 3D machine vision.Proc.of IEEE Conference of Computer Vision and Pattern Recognition 1986.
[62]G. Q.Wei and S. D.Ma.Implicit and explicit camera calibration:theory and experiment. IEEE Trans. Pattern Recognition and Machine Intelligence, 1994,16(5).
[63]Abdel-Aziz Y I,Karara H.M.Direct linear transformation from compcoordinates into object space coordinates in close-range photogrammetry. In Proceedings Symposium on Close-Range Photogrammetry,1971,1-18.
[64]Jerry Hobbs,Doug Appelto.FASTUS:a system for extracting information from natural language text[R]. Technical Report 519, SRI,1992.
[65]Linda G. Shapiro,George C. Stockman. Computer Vision[M].北京:机械工业出 版社,327-333,2005.
[66]马颂德,张正友.计算机视觉-计算理论与算法基础.北京：科学出版社,2003.
[67]Zhang Z.Flexible camera calibration by viewing a plane from unknown orientations[C].ICCV,1999:666-673.
[68]LuoGuie,Yang Xinrong.FAST AND ACCURATE CAMERA CALIBRATION METHOD BASED ON GRID-BASED BOARD PLAND. ELECTRONIC INFORMATION SCIENCE AND TECHNOLOGY.
[69]Q.-T.Luong and O.Faugeras.Self-calibration of a moving camera from point correspondences and fundamental matrices. The International Journal of Computer Vision,1997,22 (3):261-289.
[70]J. More.The levenberg-marquardt algorithm, implementation and theory.In G. A. Watson, editor, Numerical Analysis, Lecture Notes in Mathematics 630. Springer-Verlag,1977.
[71]姜大志,郁倩等.计算机视觉成象的非线性畸变研究与综述[J].计算机工程,2001,27(12)：108-110.
[72]陈胜勇,刘盛等.基于OpenCV的计算机视觉技术实现[M].北京：科学出版社, 2011：370-371.
[73]Marr D,et al. Cooperative computation of stereo disparity.Sci.1976194:283-287.
[74]Poggio T,et al.Computational vision and regularization theoery.Nature,1985, 317:314-319.
[75]C.L.Zitnick, T.Kanade, A cooperative algorithm for stereo matching and occlusion detection, Proceedings IEEE Transactions Pattern Analysis and Machine Intelligence,2000,22(7):675-684.
[76]李德广,李科杰,高丽丽.基于多尺度多方向相位匹配的立体视觉方法[J].仪器仪表学报,2004,25(4)增刊：600-602.
[77]罗桂娥,徐运彬.基于Quaternion-Gabor小波运动估计得深度图获取技术[J].计算机应用,2011.
[78]Jenkin M, et al. The measurement of binocular disparity in computational process in human vision [M]. Ablex. NewJersey:Prentice-Hall,1988.
[79]Muquit M A, Shibahara T, Aoki T. A high-accuracy passive 3D measurement system using phase-based image matching [J]. IEICE Trans on Fundamentals of Electronics, Communications and Computer Sciences,2006, E89-A(3):686-697.
[80]游素亚,柳健,万发贯.基于小波变换相位的立体匹配系统[J].信息与控制,1994,(5)：28-31.
[81]徐奕,周军,周源华.基于小波及动态规划的相位匹配[J].上海交通大学学报,2003，37(3)：388-392.
[82]Han.J.H, Park.J.S, Contour matching using epipolar geometry, IEEE Transactions on Pattern Analysis and Machine Intelligence 2000,22(4):358-370.
[83]邓志燕,陈炽坤.利用外极线约束的图像匹配新算法[J].工程图学学报,2009,5(8)：104-107.
[84]顾征,苏显渝.采用色彩相似性约束的立体匹配[J].光电工程,2007,34(1)：95-99.
[85]J.P.Semple,L.Roth. Introduction to Algebraic Geometry,Clarendon Press, Oxford,1949.
[86]Longuet-Higgins,H.C.A computer algorithm forreconstruction a scene from two projections. Nature,1981,293:133135.
[87]R.Zabih,J.Woodfill.Non-parametric local transforms for computing visual correspondence,3rd European Conference on Computer Vision,1994:151-158.
[88]白明,庄严,王伟.双目立体匹配算法的研究与进展[J].控制与决策,2008,23(7)：721-729.
[89]Scliarstein D, Szeliski R.A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algoritlun [J].International Journal of Computer Vision (S0920-5691),2002,47(1):7-42.
[90]Bobick A F, Sintille S.Large Occlusion Stereo [J].International Journal of Computer Vision (S0920-5691),1999,33(3),181-200.
[91]Lenng S C,Appleton B,Sun C.Fast Stereo Matching by Iterated Dynamic Programmurg and Quadtree Snbregioning [C.].Proceedings of the Conference on British Machine Vision. London:BMVA Press,2004:97-106.
[92]Bircbfield S,Tomasi C.Depth Discontumities by Pixel-to-Pixel Stereo [C].Proceedurgs of International Conference on Computer Vision, Bombay USA: IEEE Press,1998:1073-1080.
[93]卢阿丽,唐振民,杨静宇.一种基于双向动态规划的立体匹配算法[J].系统仿真学报,2008,2(13)：3378-3380,3384.
[94]Zhang Z,Deriche R,Faugerrau O et al.A mhust technique for matching two unccalibrated images through the recovey of the unknown epipolar geometry [J]. A rtificial Intelligence 1995,78(1-2):87-119.
[95]Zhang W,Gao X,Sung E et al.A feature-based matching scheme M PCD and robust matching strategy [J]. Pattern Recognition Letters 2007,28 (10) 1222-1231.
[96]张辉,张丽艳,陈鉴富等.基于自适应迭代松弛的立体点对匹配鲁棒算法[J].中国图象图形学报,2009,14(7)：1378-1385.
[97]Bleyer M.Gelautz M.A layered stereo algorithm using image segment-ation and global visibility constraints[J].ISPRS Journal of Photogra-mmetry and Remote Sensing,2005,59(3):128-150.
[98]Bleyer M.Gelautz M.Graph-cut-based stereo matching using image Segme-ntation Wilth Symmetrical treatment of occlusions[J].Signal Processing:Image Communication,2007,22(2):127-143.
[99]徐彦君,杜利民,侯自强等.一种用于立体匹配的改进的神经网络方法[J].中国图象图形学报,1998,3(10)：845-848.
[100]陈国良,王煦法,庄镇泉.等遗传算法及其应用[M].北京：人民邮电出版社,1996.
[101]陈宁江,李介谷,邓雁萍.遗传算法在立体图象匹配中的应用[J].上海交通大学学报,1998.32(9)：49-52.
[102]罗桂娥,杨欣荣.黄维.基于分块变换-融合滤波的改进EZW算法[J].计算机工程,2009,35(6)：225-227.
[103]杨玉娟,都思丹.分层正交动态规划立体匹配算法[J].中国科学技术大学学报,2009,39(11)：1171-1176.
[104]Gyung-Bum Kim,Sung-Chong.An accurate and robust stereo matching algorithm with variable windows for 3D measurements..Mechatronics,2004,14, 715-735.
[105]Kanade T, Okutomi M. A stereo matching algorithm with an adaptive window:Theory and experiment. IEEE Transactions on Pattern Analysis and Machine Intelligence,1994,16:920-932.
[106]Veksler O. Fast variable window for stereo correspondence using integral images.In:Processdings for the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA,2003,556-561.
[107]LuoGuie,Yang Xinrong,Xu Qi. Fast stereo matching algorithm using adaptive window. ISIP 2008 and WMWA 2008:25-30.
[108]许奇,罗桂娥,刘献如.基于整数图像技术的自适应窗口立体匹配算法[J].计算机仿真,2007,24(11)：218-220.
[109]陈国良,王煦法等.遗传算法及其应用[M].北京：人民邮电出版社,2006.
[110]吕朝辉,张兆扬,安平.一种基于遗传算法的立体匹配方法[J].计算机工程2003,29(20)：24-25,30.
[111]陈宁江.李介谷.邓雁萍.遗传算法在立体图象匹配中的应用[J].上海交通大学学报,1998,32(9)：49-52.
[112]王彪,王金岩,何亦征,沈春林.一种立体匹配的新算法[J].计算机工程,2005,.31(10)：153-155.
[113]汪剑鸣,许镇琳.浮点遗传算法中一种新的杂交算子[J].控制理论与应用,2002,19(6)：977-980.
[114]Davis L.Handbook of Genetic Algorithms. New York:Van Nostrand Reinhold, 1991.
[115]Fogel D B.An Introduction to Simulated Evolutionary Optimization. IEEE Transactions on Neural Networks,1994,5(1):3-14.
[116]梁俊明.遗传算法研究：[硕士学位论文].西安：西安电子科技大学,2002.
[117]Ming-Lun Gong, Yee-Hong Yang. Genetic-based stereo algorithm and disparity map evaluation. International Journal of Computer Vision,2002,47(1/2/3),63-77.
[118]周东翔,蔡宣平,孙茂印.一种基于特征约束的立体匹配算法[J].中国图像图形学报,2001,6(7)：653-656.
[119]贾波,章毓晋,林行刚.视差图误差检测与校正的通用快速算法[J].清华大学 学报(自然科学版),2000,40(1)：28-31.
[120]Lindeberg T.Feature detection with automatic scale selection[J].International Journal of Computer Vision,1998,30(2):79-116.
[121]Lindeberg T. Edge detection and ridge detection with automatic scale selection [J]. International Journal of Computer Vision,1998,30(2):117-156.
[122]Mikolajczyk K,Schmid C.Scale and affine invariant interestpoint detectors[J].International Journal of Computer Vision,2004,60(1)63-66.
[123]程邦胜,唐孝威Harris尺不变性关键点检测子的研究[J].浙江大学学报：工学版,2009,43(5)：855-859.
[124]Harris C, Stevens M.A combined comer and edge detector [C].Proceedings of the 4th Alvey Vision Conference,UK:the Plessey Company,1988:147-151.
[125]A.Fusiello, V.Roberto, Efficient stereo with multiple windowing, IEEE Conference on Computer Vision and Pattern Recognition,1997,858-863.
[126]H.Hirschmuller,Improvements in real-time correlation-based stereo vision, Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision,2001.
[127]黄帅,吴克伟,苏菱.基于H arris尺度不变特征的图像匹配方法[J].合肥工业大学学报(自然科学版),2011,34(3).
[128]Burt P,Julesz B.Modifications of the classical notion of Panum's fusional area[J].Percept,1980,9:671-682.
[129]Payman Moallem,Karim Faez.Search Space Reduction in the Edge Based Stereo Correspondence[J].Stuttg,2001,11:21-23.
[130]LuoGuie;Yang Xinrong; Liu, Wei.A Fast Azd Robust Based on Stero Matching Algorithm.TECHNOLOGY AND APPLICATION OF ELECTRONIC INFORMATION,2009:25-55.
[131]刘维,罗桂娥,杨欣荣.一种快速鲁棒区域匹配算法[J].微计算机信息,2009,25(11)：184-185，191.
[132]Z.Li,G. Hu.Analysis of Disparity Gradient Based Cooperative Stereo[J].IEEE Trans. On Image Procesing,1996,5:1493-1506.
[133]罗桂娥,刘小群.区域与特征相结合的立体匹配算法[J].计算机安全,2011(5)：14-17.
[134]邱阳,白立芬,李庆祥,徐毓娴.基于边缘和区域联合匹配的立体视觉新方法[J].仪器仪表学报,：2001,21(3)：255-256.
[135]高一宁,韩燮.基于Canny算子和Harris角点提取的三维重建方法[J].电子测试,2011(2)：11-16.
[136]Rui Li,Stan Sclaroff.Multi-scale 3D scene flow from binocular stereo sequences[J].Computer Vision and Image Understanding,2008(110):75-90.
[137]李海军,王洪丰,沙焕滨.平行双目视觉系统的三维重建研究[J].自动化技术与应用,2007,26(6)：56-58.
[138]郑竺英.双目立体视觉的信息加工[M].北京：科学出版社,1998.
[139]袁智,罗桂娥.基于模糊推理的方向中值椒盐滤波算法[J].微型机与应用,2010(6)：28-30.
[140]Tomasi C,Manduchi R.Biltateral filtering for gray and color images[A]. Proceedings of the 1998 IEEE International Conference on Computer Vision[C], Washington D C:IEEE Computer Society,1998.839-846.
[141]张志强,王万玉.一种改进的双边滤波算法[J].中国图象图形学报,2009,14(3)：443-447.
[142]靳明,宋建中.一种自适应的图像双边滤波方法[J].光电工程,2004,31(7)：65-67.
[143]王琼华.3D显示技术与器件[M].北京：科学出版社,2011.
[144]杨中东,雷玉堂.单CCD摄像机的一种三维自动测量系统[J].光电工程,2007,34(3),127-131.
[145]Taguchi A. Miyoshi T. Takaya Y. et al.3Dmicro-profile measurement using optical inverse scattering phase method[J]. Ann CIRP,2000,49(1):423-429.
[146]刘浩然,张文明,刘彬.基于双目立体视觉实现物体三维测量的研究[J].光子学报,2009,38(7)：1830-1834.
[147]Dave Shreiner.李军,徐波译OpenGL编程指南(第六版)[M].北京：机械工业出版社,2010.
[148]Bernardini F.Miuleman J.Rushmeier H.eL al. The Ball-Pivoting Algorithm for Surface ReconsLrucLion[J].IEEE Transactions on Visualization and Computer Graphics 1999,5 (4):349-359.
[149]Chaine R. A geometric convection approach of 3-D reconslruction[C]//E-urographics Symposium on Geometry Processing.[s.l.]:[s.n.],2003
[150]田金文,柳健,彭复员,张天序.三维可视表面重建中的内插算法[J].华中理工大学学报,1995,23(9)：24-27.
[151]Choi B K, Shin H Y, Yoon Y I et al.Triangulation of scattered data in 3D space[J]. CAD,1988,20(5):239-248.
[152]石琼,沈春林,谭皓.基于OpenGL的三维建模实现方法[J].计算机工程与应用,2004(18)122-124.
[153]高宏伟,于洋,刘晓阳.双目立体视觉三维重建实验平台研究[J].计算机工程与应用,2009,45(33)：149-152.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |