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植物形态构型三维扫描仪关键技术研究
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摘要
植物器官在生长介质中的造型和分布被称之为植物形态构型。研究植物的形态构型对研究植物的生长规律具有重要的理论意义。在农业领域,对农作物形态构型的研究对于农业生产中合理选种、配种、套种和提高农业产量等有着重要的指导作用。如何通过计算机仿真定量地测定和分析植物形态构型的参数,是农业信息化中亟需解决的问题。随着计算机视觉技术的快速发展,高精度的三维信息获取技术可以满足此类需求。
     本文研究了一种专用于植物器官形态构型的三维信息获取系统。该扫描仪基于无接触式的被动扫描方式,仅需要使用普通的相机拍摄植物器官的多角度序列图像,再对序列图像进行计算分析得到植物器官的表面点的空间坐标信息。获取的表面点三维信息可以规范化为国际标准格式输出。为此,我们设计了系统的总体方案,构建了硬件平台并开发了系统软件。在此基础上,我们重点研究了三维信息获取系统中的相机定标。
     针对3DCS500型三维扫描仪的需求,本文研究并实现了一种自动化定标系统。我们基于相机和电机的出厂软件提供的函数库进行开发,实现了在用户界面中控制电机和相机的协调运作以采集目标图像,这简化了图像采集环节所需的手动操作;编程实现了控制点信息获取(包括控制点的图像坐标值和世界坐标值)和定标参数计算的自动化。该定标系统只需少量手动操作即可获取供扫描仪直接使用的定标参数。
     定标设备因为难以实现高精度的加工和安装,会导致控制点存在异常移位误差。本文提出了一种迭代调整的控制点定位算法,用于校正这种异常误差。先使用控制点检测方法获取原始的控制点图像坐标并用于计算一个初始定标参数。接着对控制点进行反投影并计算投影点到控制点的距离,与全局的反投影均方根误差比较,根据比较的结果对控制点的图像坐标进行调整。调整过的控制点图像坐标用于计算新的定标参数,再次进行反投影计算,以此循环直到收敛。该方法能以较小的时间代价,大幅提高控制点的定位精度。
     在平面模板平移的定标方法中,定标板的装配误差会导致定标板偏离理想位置,以致产生误差。对于这种装配误差,本文提出了基于基准图像的定标校正方法和基于控制点信息特征的迭代校正方法。在基于基准图像的定标校正方法中,我们从电动旋转台中心放下一个线锤,以背景屏为背景拍摄一张锤线图像。检测锤线并获取校正点信息,代入建立的定标板歪斜模型计算歪斜系数。使用歪斜系数校正控制点世界坐标存在的误差并计算定标参数。在基于控制点信息特征的迭代校正方法中,我们建立了一个歪斜校正模型去校正定标物的歪斜误差。基于靠近定标物安装点最近的控制点的歪斜误差是非常小的以致可以忽略不计的特性,提出了一个求解歪斜系数的优化算法,提高了非线性优化的速度和稳定性。两种定标校正方法获取的定标参数提升了三维重建结果的精度。
     为了降低定标设备的费用并简化定标操作流程,本文研究了一种基于旋转定标板的平面定标方法。使用电动旋转台来带动一个平面定标板,拍摄多张不同旋转角度的定标板图像。在定标图像中获取控制点信息(包括控制点的世界坐标和图像坐标,定标板的旋转角度)并代入我们建立的相机模型,计算定标参数。该定标方法只需较简单的操作即可获取高精度的定标参数。
The modeling and distribution of plant organ in growth medium, which namely plant architecture. The study of plant architecture make an important influence in the theory of growth regular, which is playing an important role in agricultural production, reasonable selection, breeding, intercropping and improve production in agricultural area. So the determination of its parameters and analysis is crucial. It's a significant issue in digital agriculture how to quantitatively analysis and research the relationship using computer simulation. With the rapid development of computer vision technology, high-precision three-dimensional information acquisition technology may fix those problems.
     In this paper, we realize a three-dimensional information acquisition system for plant architecture. The scanner based on the non-contact surveying is introduced, which can acquires 3D geometric information of the body surface through software processing with the body shape captured by visual equipments. The system is a high-tech product of abroad application. It can acquire automatically the coordinate information of the object surface points in 3D space, build 3D models of body surface, and save the acquired data as corresponding standard interface files. Several key problems of the scanner are emphasized:overall technical scheme of the system and camera calibration.
     We design and realize the 3D scanner which marked as 3DCS500. For this aim, we design the overall structure of the scanner, in which we establish hardware platform for operation stably and quickly, and we develop the system software including the user software and it's functional module. All of those are realized with VC++development tools.
     We research on the automatic method of calibration for the scanner. We develop the control software based on the original software, by which the camera and electric motor are controlled congruously to acquire the calibration images. Control points are detected automatically and their information are used for processing calibration parameters. With a few manual operations, a set of implicit camera parameters can be obtained.
     The infinite precision of calibration equipment cause abnormal error to control points, which decrease the calibration accuracy. A novel control point detection algorithm is presented to correct this error. We detect control points in the calibration images and assign world coordinates for control point by its'spatial distribution. The initial camera parameters are calculated with the original information of control points using the Levenberg-Marquardt algorithm. Then the pixel distance between control point and it's project is compared with the overall RMS error, by which we adjust the image coordinates of control point. The adjusted control points are utilized to compute a new parameters and the comparison is processed again until convergence. This method can improve calibration accuracy with a small time consuming.
     There are always exist deviation in assemble of planar object, which cause errors in calibration.We introduce two calibration correction methods to correct this deviation. In the first methos, we take an reference image which is the image of plumb line. With the reference image the correct points are abtained. Using the correct points we calculate the skew-correction coefficient matrix in the skew model, by which the error of information of the control point is corrected. In the second method, we produce a calibration correction method based on the information feature of control points. A skew correction model of the planar object is established to amend the skew bias. We find out and prove that the skew bias of the control point which is close to the fix point is micro enough to be ignored. Based on this property, the optimization algorithm is developed to estimate the skew factors quickly and steadily. The improved accuracy of information of control points improves calibration and the implicit camera parameters can be provided, which considerably benefits 3D reconstruction.
     The calibration equipments are expensive. So we develop a novel calibration system for the plant 3D scanner. With a few manual operations, a set of implicit camera parameters can be estimated directly using this system. Control points are detected with the refinement algorithm developed by us and numbered. The information of control points are devoted into the camera model and the camera parameters can be estimated. Many experiments denote the feasibility and the accuracy of the proposed method. Comparing with manual calibration methods, this system can satisfy the demand of the plant 3D scanner, which is convenient and dependable.
引文
[1]高文.计算机视觉——算法与系统原理.北京:清华大学出版社.1999.
    [2]D.H.巴拉德,C.M.布.计算机视觉.北京:科学出版社.1987.
    [3]S. Peter.3D scanning in apparel design and human engineering. IEEE Trans. PAMI, 1996.16(5):11-15.
    [4]J, P.B. Mass Customization:The New Frontier in Business Competition. Boston: Harvard Business School Press.1993.
    [5]朱洲.三维人体信息获取及虚拟服装试穿技术研究:[博士学位论文].华中科技大学,2004.
    [6]廖红,严小龙.高级植物营养学.科学出版社.2003.
    [7]赵静,付家兵等.大豆磷效率应用核心种质的根构型性状评价.科学通报,2004.49(13):1249-1257.
    [8]高家合,曾秀成等.烟草磷效率的基因型差异及其与根系形态构型的关系.西北植物学报,2010.8:1606-1613.
    [9]刘灵,王秀荣等.不同根构型大豆对低磷的适应性变化及其与磷效率的关系.中国农业科学,2008.4:1089-1099.
    [10]梁泉,严小龙.植物根构型的定量分析.植物学通报,2007.6:695-702.
    [11]郭焱,虚拟植物的研究进展.科学通报,2001.4:273-280.
    [12]姜丽萍,虚拟植物的研究进展.农机化研究,2006.4:4-6.
    [13]P. A. Wilson, S. C. The virtual plant:a new tool for the study and management of plant diseases. Crop Protection,1998.17(3):231-239.
    [14]袁清华,陈达刚等.大豆根系性状和磷效率的遗传规律研究.大豆科学,2006.2:158-163.
    [15]刘鹏,王金祥.磷有效性与植物侧根的发生发育.植物生理学通讯,2006.3:395-400.
    [16]向子云,周学成.多层螺旋CT三维成像技术观测植物根系的实验研究.CT理论与应用研究,2006.3:1-5.
    [17]James Clarke, L. C. E-TAILOR:Integration of 3D Body Measurement,Advanced CAD, and E-Commerce Technologies in the European Fashion Industry. International Journal of e-Business Strategy Management,2001.2(3):201-209.
    [18]McKinnon, C.I. Consumer Acceptance of Body Scanning. Journal of Fashion Marketing and Management,2001.3(2):78-89.
    [19]张广军.视觉测量.北京:科学出版社.2008.
    [20]马颂德.计算机视觉——计算理论与算法基础.北京:科学出版社.1998.
    [21]吴险峰.基于三维激光彩色扫描仪关键技术研究:[博士学位论文].华中科技大学,2004.
    [22]叶建辉.三维重建及简化技术研究:[博士学位论文].华中科技大学,2003.
    [23]金刚.三维扫描仪三维信息获取理论与技术研究:[博士学位论文].华中科技 大学,2002.
    [24]熊联欢.三维彩色信息获取的原理、算法的研究:[博士学位论文].华中理工大学,1999.
    [25]李清光.三维人体扫描仪关键技术研究:[博士学位论文].华中科技大学,2008.
    [26]Xia, D., Li Dehua. A novel approach for computing exact visual hull from silhouettes. International Journal for Light and Electron Optics.2011,27(1): 234-242.
    [27]胡寅.三维扫描仪与逆向工程关键技术研究:[博士学位论文].华中科技大学,2005.
    [28]朱洲.三维人体信息获取及虚拟服装试穿技术研究.2004,华中科技大学.
    [29]YI Abdel-Aziz, H.M.K., Direct linear transformation into object space coordinates in close-range photogrammetry. Symposium on Close-Range Photogrammetry, 1971.5:66-76
    [30]冯文灏.工业测量——武汉大学学术丛书.北京:武汉大学出版社.2004.
    [31]王卫宁,梁镜明,张存林.全息干涉法在表面封装组件质量检测中的应用.激光技术,2000.24(1):15-19.
    [32]蒋向前,李柱,谢铁邦.全息光栅干涉法测量曲面形貌的理论研究.华中理工大学学报,1994.22(2):45-51.
    [33]金刚,陈振羽,宋昆.几种三维扫描设备.计算机世界,1999.7(26):52-53.
    [34]强玉俊,蒋大真,盛康龙,工业CT研制进展.核物理动态,1994.11(4):28-31.
    [35]G. Slabaugh, B.C., T. Malzbenderet. A survey of methods for volumetric scene reconstruction from photographs. International Workshop on Volume Graphics, 2001.12:81-100.
    [36]刘钢,基于图像序列的几何和纹理重建技术研究.2004,浙江大学.
    [37]Chaumett, S.Boukir, P.Bouthemy. Structure from Controlled Motion. IEEE Trans. PAMI,1996.18(5):492-504.
    [38]L. Chen, K.Y.K.W.3D reconstruction using silhouettes from unordered viewpoints. Image Vision Comput,2010.5(28):178-184.
    [39]J.S. Franco, E.B. Efficient polyhedral modeling from silhouettes. IEEE Trans. PAMI,2009.11(31):192-204.
    [40]B.K.P. Horn, J.B. The Variational Approach to Shape from Shading. Graphics and Image Processing,1986.33:174-188.
    [41]Wang, F. Y. An efficient coordinate frame calibration method for 3-D measurement by multiple camera systems. Systems,Man and cybernetics-part c:applications and reviews,2005.35(4):452-464.
    [42]Nunzio Alberto Borghese, G.F. Autoscan:A Flexible and Portable 3D Scanner. IEEE Computer Graphics and Applications,1998.4:1-5.
    [43]Shaffer, M.Ga.E. A Multiphase Approach to Efficient Surface Simplification. In Proceedings of IEEE Visualization,2002.12:21-32.
    [44]金刚.三维激光彩色扫描仪中目标的空间及色彩信息获取:[硕士学位论文].华中科技大学,1997.
    [45]Ye Jianhui, L.D. Simplification of 3D head mesh acquired from laser scanner. Machine GRAPHICS & VISION,2002.16(3):88-96.
    [46]Qing-guang Li, C.G., Yin Hu, De-hua Li. An Application of Neural Network Optimized by Genetic-Simulated Annealing Hybria Algorithm for Data Mending. International Journal of Advances in System Science and Application,2007.12(2): 364-372.
    [47]http://www.visint.con/.
    [48]http://www.turing.gla.ac.uk/.
    [49]www.llnl.gov/sensor technologv/STR51.html.
    [50]http://www.research.microsoft.com/research/vision/szeliski.
    [51]G. Slabaugh, B.C., T. Malzbenderet. A survey of methods for volumetric scene reconstruction from photographs. International Workshop on Volume Graphics, 2001.2(31):81-100.
    [52]Isao Miyagawa, H.A. Simple Camera Calibration From a Single Image Using Five Points on Two Orthogonal 1-D Objects. IEEE Transactions on Image Processing, 2010.6(19):344-354.
    [53]Li Qing-guang, G. C, Wu Xian-feng, Li De-hua. A Method for Range Image Registration Based on Neural Network and ICP Algorithm. Journal of Wuhan university,2008.21:121-129.
    [54]Qing-guang Li, C. G., Yin Hu, De-hua Li. An Application of Neural Network Optimized by Genetic-Simulated Annealing Hybria Algorithm for Data Mending. International Journal of Advances in System Science and Application,2008.12(3): 441-451.
    [55]F.H. Shi, J.H.W., J. Zhang. Motion Segmentation of Multiple Translating Objects Using Line Correspondences. Computer Vision and Pattern Recognition,2005, 1(4):315-320.
    [56]O.Faugeras, N. N., and R. Deriche. On the information contained in the motion field of lines and the cooperation between motion and stereo. Imaging Systems and Technology,1991.2(3):21-32.
    [57]Brown, D.C. Decentering distortion of lenses. Photogrammetric Engineering,1966. 3(32):45-49.
    [58]Faig, W. Calibraiton of close-range photogrammetric systems:mathematical formulation. Photogrammetric eng.Remote sensing,1975.21:88-98.
    [59]H. A. Martins, J.R.B. Camera models based on data from two calibration plane. Computer Graphics and Image Processing,1981.21(3):251-265.
    [60]Tsai, R.Y. An efficient and accurate camera calibration technique for 3D machine vision. CVPR,1986.11:321-331.
    [61]Tsai, R.Y. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal of Robotics and Automation,1987.5(7):335-365.
    [62]Faugeras O., L.O.T. Camera self-calibration:Theory and experiments. ECCV, 1992.12:88-96.
    [63]Janne Heikkila, O.S. A Four-step Camera Calibration Procedure with Implicit Image Correction. CVPR,1997.3(5):187-199.
    [64]S.D., M. A self-calibration technique for active vision system. IEEE Trans Robotics and Automation,1996.22:56-67.
    [65]G. Q. Wei, S.D.M. Implicit and Explicit Camera Calibration:Theory and Experiment. IEEE Trans. PAMI,1994.5(16):54-62.
    [66]Faugeras, O.. Three-Dimensional Computer Vision:a Geometric Viewpoint. MIT Press.1993.
    [67]P. Sturm. On plane-based camera calibration:A general algorithm, singularities, applications. CVPR,1999.2(8):87-101.
    [68]Heikkila, J. Geometric camera calibration using circular control points. IEEE Trans. PAMI,2000.10(22):121-132.
    [69]Zhang, Z. A flexible new technique for camera calibration. IEEE Trans. PAMI, 2000.11(22):77-89.
    [70]Beiwei Zhang, Y.F.L. Planar Pattern for Automatic Camera Calibration. Opt. Eng, 2003.6(42):161-172.
    [71]Q. Chen, H.W., T. Wada. Camera calibration with two arbitrary coplanar circles. ECCV,2004.21:77-86.
    [72]Qiang Ji, M. and Songtao Dai. Self-Calibration of a Rotating Camera With a Translational Offset. IEEE transactions on robotics and automation,2004.1(20): 191-203.
    [73]Zhang, Z. Camera Calibration with One-Dimensional Objects. IEEE Trans. PAMI, 2004.7(26):96-105.
    [74]J.-S. Kim, P.G., and I.-S. Kweon. Geometric and algebraic constraints of projected concentric circles and their applications to camera calibration. IEEE Trans. PAMI, 2005.4(27):321-331.
    [75]Jiang G., Q.L. Detection of concentric circles for camera calibration. ICCV,2005.
    [76]C. Colombo, D.C. Camera calibration with two arbitrary coaxial circles. ECCV, 2006.2(11):21-32.
    [77]X. Cao, H.F. Camera calibration using symmetric objects. IEEE Trans. Image Process,2006.11(15):87-97.
    [78]Dong L. P., L. D.H. A Camera Calibration Method Based on DLT Model and Circular Points Correction. Computer and Engineering Institute,2007.35(9): 144-150.
    [79]L. Wang, F.C.W., Z.Y. Hu. Multi-camera calibration with one-dimensional object under general motions. ICCV,2007.22:44-56.
    [80]Sun Xianbin, L.D., Y.J. Calibration for the 3D Colour Scanner System. J. Wuhan Univ.(Nat. Sci. Ed.),2007.5(53):77-90.
    [81]Xiao Chen He, N.H.C.Y. Corner detector based on global and local curvature properties. Opt. Eng,2008.5(47):251-262.
    [82]Ankur Datta, J.K. Accurate Camera Calibration using Iterative Refinement of Control Points. ICCV,2009.24:88-98.
    [83]En Peng, L.L. Camera calibration using one-dimensional information and its applications in both controlled and uncontrolled environments. Pattern Recognition, 2010.2(43):141-152.
    [84]Bouguet. Camera Calibration Toolbox for MATLAB. http://www.vision.caltech.edu/bouguet/calibdoc/.2011.
    [85]Craig S. Anderson, A.E.M. ACIS Sub-Pixel Resolution:Improvement in Point Source Detection. Astronomical Data Analysis Software and Systems,2011. 12(442):451-462.
    [86]X. Meng, Z.H. A new easy camera calibration technique based on circular points. Pattern Recognition,2003.36(5):301-312.
    [87]Luh JY, K.L.J. A three dimensional vision by off shelf system with mutli-camera. IEEE Trans. PAMI,1985.7(1):201-216.
    [88]D. Daucher, M.D., J, Lapreste. Camera calibration form spheres images. In Proc. of European Conf. Computer Vision,1994:449-454.
    [89]H. Zhang, K.W., G. Zhang. Camera calibration from images of spheres. IEEE Trans. PAMI,2007.3:499-503.
    [90]Keith Forbes, A.V.N.B. An Inexpensive, Automatic and Accurate Camera Calibration Method. PRASA2002.12:96-103.
    [91]A. Fitzgibbon, M.P. Direct least square fitting of ellipses. ECCV,1999.5(21): 544-560.
    [92]Y. Wu, X.L., F. Wu, Z. Hu. Coplanar circles, quasi-affine invariance and calibration.24,2006.4:319-326.
    [93]F. Wu, Z.H., H, Zhu. Camera calibration with moving one-dimensional objects. Pattern Recognition,2005.38(5):755-765.
    [94]Maybank S, F.O. A theory of self-calibration of moving camera. International Journal of Computer Visio,1992.8(2):123-151.
    [95]S. C. Bae, I.S.K. COP:a new corner detector. Pattern Recogn. Lett.,2002. 11(23):361-372.
    [96]Y. Xiao. Accurate Feature Extraction and Control Point Correction for Camera Calibration with a Mono-Plane Target, in 3DPVT 2010.12:441-448.
    [97]陈光.亚像素级角点提取算法:[博士学位论文].吉林大学,2009.
    [98]C. Harris, M.S. A combined corner and edge detector, in Fourth Alvey Vision Conf. 1988.3:96-104.
    [99]More J.J. The Levenberg-Marquardt Algorithm:Implementation and Theory. Springer,1977.
    [100]P.A.L. Hendrik, H.W., S.Hans-Peter. Asilhouette-basedalgorithmfor texture registrationandstitching. Graph.Models,2001.63(4):245-262.
    [101]L. Chen. Exact visual hull from marching cubes. Thirdlnt.Conf. Comput. VisionTheory.Appl.,2008:597-604.

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