新一代人机交互中基于多视点图像的三维信息获取研究
|
摘要
通过二维图像获取三维信息是计算机视觉的主要研究目标之一,随着其应用的不断扩大和深入,新的科技需求和科学问题不断出现,其中从多视点图像、序列图像与无序图像集(包括网络图像)中获取三维信息更是当前的主流方向,特别对未定标的图像系统,难度更大。本文根据研究第四代人机交互中AVR (from actual to virtual reality)三维虚拟现实的要求,着重研究了涉及多视点图像、序列图像与无序图像集(包括网络图像)中获取三维信息的若干有关提高三维重建精度的问题。在高精度相机定标、从射影重建到度量重建中自定标、定标参数的统计优化理论与算法方面取得了一定的成果,并建立了一个AVR的重建原型系统(3DPOM).论文的主要贡献如下：
1.在高精度相机定标方面,针对图像中表面纹理不丰富的景物需要借助人工标记进行辅助特征匹配的问题,提出了一类编码圆环标记的检测与识别系统方法。该方案引入同态滤波压抑了光照变化对图像二值化的影响;在对标记成像边缘检测时,提出了1D边缘检测法,从而能够快速、准确地拟合出标记中心位置；在对编码区域译码时,提出了仿射变换矫正与极坐标变换相结合的方法,降低了成像形变造成的译码错误率。整个系统具有抗光照变化鲁棒性强、标记中心定位精度高和译码准确等特点
2.对多视点图像三维重建的精度提高问题,对固定焦距相机的自定标,提出了基于LMI (Linear Matrix Inequality)松弛技术的多视点固定焦距值全局优化求解方法。以往不少研究者从本征矩阵出发讨论了如何从两视点中估计焦距值,本文借助绝对对偶二次曲面给出了从多视点中进行固定焦距值的全局优化求解方法。该方法在绝对对偶二次曲面的基本成像约束方程基础上,把固定焦距作为独立未知量纳入到目标函数中,构造了带约束的多项式极值问题,通过使用基于LMI松弛的优化方法获得了焦距的全局最优解。合成数据和真实图像实验均表明本文方法是有效的,具有计算时间快和鲁棒性好的优点。
3.在长序列图像的三维重建过程中容易出现误差积累问题,针对焦距固定相机且未定标情况,提出了长序列图像相机焦距的最优估计和度量重建结果的层次融合式重建方法。该方法利用焦距固定和序列较长的两个特点,把重建的各个阶段紧密有机地整合在一起,通过焦距参数的分组自标定与集中投票相结合的方式,得到了概率意义下的最优焦距值,减小了焦距估计的偏差。在对度量重建结果进行融合时,提出了极小化基于L∞范数表示的重投影误差的融合算法,克服了以往极小化基于L2范数表示的重投影误差容易陷入局部极值的缺点。实验表明重建结果具有较高的准确度,可满足基于长序列图像的建模、绘制以及增强现实等应用的要求。
4.针对无序未定标图像集合中进行三维重建所面临的图像组织和相机鲁棒自定标问题,捉出了基于生成树层次剖分的射影重建层次融合和鲁棒绝对对偶二次曲面估计方法。在图像有序化过程中,构造了两视点几何关系监督下的生成树,为了减少误差积累对投影矩阵精确性造成的不利影响,在生成树剖分的基础上实现了图像的群组划分,按照与层次剖分相反的次序把各个群组内的射影重建结果融合在一起。为了将射影重建正确转换为度量重建,把有无穷远平面约束的基于绝对对偶二次曲面的自定标纳入RANSAC计算框架下,提高了自定标成功率。实验结果表明生成树层次融合三维重建方法可以对无序图像进行正确的相机位置估计和场景结构恢复。
在以上各部分的合成与真实数据实验验证的基础上,最后实现了一个实用简便、精度高、真实感强的模型重建原型系统3DPOM,作为AVR实验平台和总结性的成果,为今后进一步的研究打下了坚实的基础。
One essential and important task in computer vision is capturing accurate3D information of an object from images. With the continuous expansion of its application and in-depth, new technology needs and emerging scientific problems continue to emerge. Getting more and more accurate information from multi-view, sequential and unordered (internet) images becomes one of the mainstream. It is difficult especially for uncalibrated camera system. In this dissertation we discuss the problem of high accurate three-dimensional reconstruction from multi-view, sequential and unordered images according to the requirement of research on AVR (from actual to virtual reality) conceptual framework in the new (the fourth) generation human-computer interaction. Some results have been achieved on high accurate camera calibration, self-calibration for upgrading from a projective to a metric framework and statistical optimization for calibrated parameters. A prototype system (3DPOM) adapted to AVR is designed and implemented. The main contributions are as follows:
1. For high accurate camera calibration, an approach is proposed for better detection and recognition of coded concentric rings used as camera calibration target when areas of low texture in an image are large. The homomorphic processing is applied to attack the thresholding problem of image binarization arisen from different illumination. The1D edge detection method is proposed to find the points on the target edges. The accuracy and speed of ellipse fitting from edge-points are improved. The affine and polar coordinate transformation are combined to reduce the imaging deformation and decrease the decoding error rate. The new approach has the characteristics of higher accuracy of target location and more robustness against different illumination.
2. For improving the accuracy in3D reconstruction from multi-view images, a method based on LMI (Linear Matrix Inequality) relaxation technique is proposed to find the globally optimal solution to the multiple views constant focal length self-calibration problem. Most previous work used Essential Matrix for two views constant focal length self-calibration. Our approach is based on the explicit constraints related absolute dual quadric with its multiple view images and the global optimization to avoid the local optimum. The constrained polynomial minimization problems are formed with respect to two types of parametrization on absolute dual quadric, and solved by LMI relaxation optimization method, Experiments with simulated data and real images show that our approach works quite well.
3. To avoid error accumulation for long image sequences in3D reconstruction, a statistical estimation method and hierarchical merging scheme of metric reconstruction for constant focal length camera are proposed under the conditions of fixed but unknown camera intrinsic parameters. All reconstruction stages are connected coherently according to these two conditions of constant focal length and long image sequences. A combination of self-calibration from different image groups and voting is adopted to get the optimal estimation of constant focal length in statistics. The reprojection error based on L∞-norm instead of L2-norm, which is easy to get stuck at local minima, is minimized to merge two sub-sequences. Several experiments on simulated and real sequences demonstrate the merit of the accuracy of3D reconstruction.
4. For unordered image sets there exists problem of how to organize the image set and improve the robustness of self-calibration of camera. Solving these problems a new3D reconstruction method is proposed which is based on hierarchical partition and merging of spanning tree. Under the supervision of two view geometry we construct the spanning tree of the relationship graph among images. Then the images are grouped for a merging-based projective reconstruction according to the partition of spanning tree. The above process of projective reconstruction may distribute any error as evenly over the images as possible, thereby reducing camera drift. A novel approach to self-calibration, using a RANSAC-based random sampling algorithm to estimate the absolute dual quadric (ADQ) with infinite plane constraint is implemented to increase the usefulness and applicability. Experimental results show that the method gives correct scene structure and camera motion across unordered image sets.
On the basis of the feasibility and effectiveness of above methods and algorithms, a prototype system (3DPOM) for photorealistic modeling with promising accuracy is introduced to summarize this work. The platform lays a sound basis for our further research of AVR.
1.在高精度相机定标方面,针对图像中表面纹理不丰富的景物需要借助人工标记进行辅助特征匹配的问题,提出了一类编码圆环标记的检测与识别系统方法。该方案引入同态滤波压抑了光照变化对图像二值化的影响;在对标记成像边缘检测时,提出了1D边缘检测法,从而能够快速、准确地拟合出标记中心位置；在对编码区域译码时,提出了仿射变换矫正与极坐标变换相结合的方法,降低了成像形变造成的译码错误率。整个系统具有抗光照变化鲁棒性强、标记中心定位精度高和译码准确等特点
2.对多视点图像三维重建的精度提高问题,对固定焦距相机的自定标,提出了基于LMI (Linear Matrix Inequality)松弛技术的多视点固定焦距值全局优化求解方法。以往不少研究者从本征矩阵出发讨论了如何从两视点中估计焦距值,本文借助绝对对偶二次曲面给出了从多视点中进行固定焦距值的全局优化求解方法。该方法在绝对对偶二次曲面的基本成像约束方程基础上,把固定焦距作为独立未知量纳入到目标函数中,构造了带约束的多项式极值问题,通过使用基于LMI松弛的优化方法获得了焦距的全局最优解。合成数据和真实图像实验均表明本文方法是有效的,具有计算时间快和鲁棒性好的优点。
3.在长序列图像的三维重建过程中容易出现误差积累问题,针对焦距固定相机且未定标情况,提出了长序列图像相机焦距的最优估计和度量重建结果的层次融合式重建方法。该方法利用焦距固定和序列较长的两个特点,把重建的各个阶段紧密有机地整合在一起,通过焦距参数的分组自标定与集中投票相结合的方式,得到了概率意义下的最优焦距值,减小了焦距估计的偏差。在对度量重建结果进行融合时,提出了极小化基于L∞范数表示的重投影误差的融合算法,克服了以往极小化基于L2范数表示的重投影误差容易陷入局部极值的缺点。实验表明重建结果具有较高的准确度,可满足基于长序列图像的建模、绘制以及增强现实等应用的要求。
4.针对无序未定标图像集合中进行三维重建所面临的图像组织和相机鲁棒自定标问题,捉出了基于生成树层次剖分的射影重建层次融合和鲁棒绝对对偶二次曲面估计方法。在图像有序化过程中,构造了两视点几何关系监督下的生成树,为了减少误差积累对投影矩阵精确性造成的不利影响,在生成树剖分的基础上实现了图像的群组划分,按照与层次剖分相反的次序把各个群组内的射影重建结果融合在一起。为了将射影重建正确转换为度量重建,把有无穷远平面约束的基于绝对对偶二次曲面的自定标纳入RANSAC计算框架下,提高了自定标成功率。实验结果表明生成树层次融合三维重建方法可以对无序图像进行正确的相机位置估计和场景结构恢复。
在以上各部分的合成与真实数据实验验证的基础上,最后实现了一个实用简便、精度高、真实感强的模型重建原型系统3DPOM,作为AVR实验平台和总结性的成果,为今后进一步的研究打下了坚实的基础。
One essential and important task in computer vision is capturing accurate3D information of an object from images. With the continuous expansion of its application and in-depth, new technology needs and emerging scientific problems continue to emerge. Getting more and more accurate information from multi-view, sequential and unordered (internet) images becomes one of the mainstream. It is difficult especially for uncalibrated camera system. In this dissertation we discuss the problem of high accurate three-dimensional reconstruction from multi-view, sequential and unordered images according to the requirement of research on AVR (from actual to virtual reality) conceptual framework in the new (the fourth) generation human-computer interaction. Some results have been achieved on high accurate camera calibration, self-calibration for upgrading from a projective to a metric framework and statistical optimization for calibrated parameters. A prototype system (3DPOM) adapted to AVR is designed and implemented. The main contributions are as follows:
1. For high accurate camera calibration, an approach is proposed for better detection and recognition of coded concentric rings used as camera calibration target when areas of low texture in an image are large. The homomorphic processing is applied to attack the thresholding problem of image binarization arisen from different illumination. The1D edge detection method is proposed to find the points on the target edges. The accuracy and speed of ellipse fitting from edge-points are improved. The affine and polar coordinate transformation are combined to reduce the imaging deformation and decrease the decoding error rate. The new approach has the characteristics of higher accuracy of target location and more robustness against different illumination.
2. For improving the accuracy in3D reconstruction from multi-view images, a method based on LMI (Linear Matrix Inequality) relaxation technique is proposed to find the globally optimal solution to the multiple views constant focal length self-calibration problem. Most previous work used Essential Matrix for two views constant focal length self-calibration. Our approach is based on the explicit constraints related absolute dual quadric with its multiple view images and the global optimization to avoid the local optimum. The constrained polynomial minimization problems are formed with respect to two types of parametrization on absolute dual quadric, and solved by LMI relaxation optimization method, Experiments with simulated data and real images show that our approach works quite well.
3. To avoid error accumulation for long image sequences in3D reconstruction, a statistical estimation method and hierarchical merging scheme of metric reconstruction for constant focal length camera are proposed under the conditions of fixed but unknown camera intrinsic parameters. All reconstruction stages are connected coherently according to these two conditions of constant focal length and long image sequences. A combination of self-calibration from different image groups and voting is adopted to get the optimal estimation of constant focal length in statistics. The reprojection error based on L∞-norm instead of L2-norm, which is easy to get stuck at local minima, is minimized to merge two sub-sequences. Several experiments on simulated and real sequences demonstrate the merit of the accuracy of3D reconstruction.
4. For unordered image sets there exists problem of how to organize the image set and improve the robustness of self-calibration of camera. Solving these problems a new3D reconstruction method is proposed which is based on hierarchical partition and merging of spanning tree. Under the supervision of two view geometry we construct the spanning tree of the relationship graph among images. Then the images are grouped for a merging-based projective reconstruction according to the partition of spanning tree. The above process of projective reconstruction may distribute any error as evenly over the images as possible, thereby reducing camera drift. A novel approach to self-calibration, using a RANSAC-based random sampling algorithm to estimate the absolute dual quadric (ADQ) with infinite plane constraint is implemented to increase the usefulness and applicability. Experimental results show that the method gives correct scene structure and camera motion across unordered image sets.
On the basis of the feasibility and effectiveness of above methods and algorithms, a prototype system (3DPOM) for photorealistic modeling with promising accuracy is introduced to summarize this work. The platform lays a sound basis for our further research of AVR.
引文
[1]A. P. Pentland, R. Cipolla, A. Pentland. Smart Rooms:Machine Understanding of Human Behavior. Cambridge University Press.1998
[2]S. Pastoor, L. Jin, S. Renault. An experimental multimedia system allowing 3-D visualization and eye-controlled interaction without user-worn devices. Multimedia, IEEE Transactions on. 1999.1(1).41-52
[3]M. Aakay, I. Marsic, A. Medl, B. Guangming. A system for medical consultation and education using multimodal human/machine communication. Information Technology in Biomedicine, IEEE Transactions on.1998.2(4).282-291
[4]A. Cheyer, L. Julia. Multimodal Maps:An Agent-based Approach. International Conference on Cooperative Multimodal Communication (CMC/95). Eindhoven, The Netherlands.1995.
[5]N. Magnenat-Thalmann, S. Kshirsagar. Communicating with Autonomous Virtual Humans. Proc. of the 17th TWENTE Workshop on Language TechnologyEaschede. Universiteit Twente. 2000.1-8
[6]莫庆远.超级智能视听信息处理系统环境的研究[博士学位论文].信息科学研究所,北方交通大学.1992
[7]M. Zhenjiang, J.-L. Basille, B. Yuan. OOPR:A Novel Theory for Multimedia Information Processing. Proc. of Sixth International Conference on Signal Processing Applications and Technology. Boston, USA.1995.
[8]阮秋琦,袁保宗.智能视听信息处理的新策略.北方交通大学学报.1996.20(2).152-160
[9]高文,袁保宗.多功能感知机.国家自然科学丛金重点项目申请书(内部报告).1998
[10]袁保宗,阮秋琦,王延江,刘汝杰,唐晓芳.新一代(第四代)人机交互的概念框架特征及关键技术.电子学报.2003.31(12A).1945-1954
[11]王延江.新一代(第四代)人机交互的概念框架及其关键技术研究[博士学位论文].北方交通大学.2001
[12]孟祥旭,李学庆.人机交互技术：原理与应用.北京.清华大学出版社.2004
[13]邓世伟.基于CV/CAD的三维物体AVR建模[博士学位论文].信息科学研究所,北方交通大学.1994
[14]B. Yuan, C. Zhang, X. Tang. AVR Theory and Applications:Virtualizing the Real World Objects. JCSSP. Tokyo.1997.103-108
[15]刘渭滨.从真实世界到虚拟现实：AVR与虚拟环境建模的研究与实现[博士学位论文].信息科学研究所,北方交通大学.2001
[16]胡明星.未定标系统下几何估计、摄像机定标与三维重建研究[博士学位论文].计算机学院,北京交通大学.2003
[17]张超.三维曲面物体的计算机视觉表示、造型和重建的研究与实现[博上学位论文].信息科学研究所,北方交通大学.1994
[18]邢薇薇.AVR与三维物体识别的研究与实现[博上学位论文].计算机学院,北京交通大学.2006
[19]Y. I. Abdel-Aziz, H. M. Karara. Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. Proceedings of the Symposium on Close-Range Photogrammetry.1971. American Society of Photogrammetry.1-18
[20]Z. Zhang. A flexible new technique for camera calibration. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2000.22(11).1330-1334
[21]Y. Wu, X. Li, F. Wu, Z. Hu. Coplanar circles, quasi-affine invariance and calibration. Image and Vision Computing.2006.24(4).319-326
[22]Y. Xianghua, Z. Hongbin. A Novel Linear Approach to Camera Calibration from Sphere Images. Pattern Recognition,2006. ICPR 2006.18th International Conference on.2006.535-538
[23]Z. Zhang. Camera calibration with one-dimensional objects. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2004.26(7).892-899
[24]王亮,吴福朝.基于一维标定物的多摄像机标定.自动化学报.2007.33(3).
[25]P. Torr, A. Zisserman. Feature Based Methods for Structure and Motion Estimation. Vision Algorithms:Theory and Practice.2000.278-294
[26]D. G. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision.2004.60(2).91-110
[27]S. Jianbo. C. Tomasi. Good features to track. Computer Vision and Pattern Recognition,1994. Proceedings CVPR'94.,1994 IEEE Computer Society Conference on.1994.593-600
[28]J. Canny. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 1986.8(6).679-698
[29]C. Schmid, A. Zisserman. Automatic line matching across views. Computer Vision and Pattern Recognition,1997. Proceedings.,1997 IEEE Computer Society Conference on.1997.666-671
[30]B. Lucas, T. Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. Proc. of 7th International Joint Conference on Artificial Intelligence (IJCAI).1981.
[31]M. A. Fischler, R. C. Bolles. Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM.1981.24(6). 381-395
[32]R. I. Hartley. In defense of the eight-point algorithm. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1997.19(6).580-593
[33]Z. Zhang, D. R., F. O., Q.-T. L. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry Artificial Intelligence.1995.78(1). 87-119
[34]Q.-T. Luong, O. D. Faugeras. The fundamental matrix:Theory, algorithms, and stability analysis. International Journal of Computer Vision.1996.17(1).43-75
[35]L. Quan. Invariants of six points and projective reconstruction from three uncalibrated images. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1995.17(1).34-46
[36]P. H. S. Torr, A. Zisserman. Robust parameterization and computation of the trifocal tensor. Image and Vision Computing.1997.15(8).591-605
[37]R. Szeliski. Computer Vision:Algorithms and Applications. Springer.2010
[38]P. A. Beardsley, A. Zisserman, D. W. Murray. Sequential Updating of Projective and Affine Structure from Motion. International Journal of Computer Vision.1997.23(3).235-259
[39]P. Sturm, B. Triggs. A factorization based algorithm for multi-image projective structure and motion. Computer Vision — ECCV '96.1996.709-720
[40]S. Christy, R. Horaud. Euclidean Shape and Motion from Multiple Perspective Views by Affine Iterations. IEEE Trans. Pattern Anal. Mach. Intell.1996.18(11).1098-1104
[41]F. Kahl. Multiple View Geometry and the L-infinity Norm. Computer Vision,2005.ICCV 2005. Tenth IEEE International Conference on.2005.1002-1009 Vol.2
[42]A. W. Fitzgibbon, A. Zisserman. Automatic Camera Recovery for Closed or Open Image Sequences. Computer Vision — ECCV '98.1998.311
[43]H.-Y. Shum, Q. Ke, Z. Zhang. Efficient bundle adjustment with virtual key frames:a hierarchical approach to multi-frame structure from motion. Computer Vision and Pattern Recognition,1999. IEEE Computer Society Conference on.1999.543 Vol.2
[44]O. D. Faugeras, Q.-T. Luong, S. J. Maybank. Camera Self-Calibration:Theory and Experiments. Proceedings of the Second European Conference on Computer Vision.1992. Springer-Verlag.
[45]L. Agapito, E. Hayman, I. Reid. Self-Calibration of Rotating and Zooming Cameras. International Journal of Computer Vision.2002.47(1).287-287
[46]M. Pollefeys, R. Koch, L. V. Gool. Self-Calibration and Metric Reconstruction Inspite of Varying and Unknown Intrinsic Camera Parameters. International Journal of Computer Vision. 1999.32(1).7-25
[47]B. Triggs, F. M. Philip, I. H. Richard, W. F. Andrew. Bundle Adjustment - A Modern Synthesis. Proceedings of the International Workshop on Vision Algorithms:Theory and Practice.2000. Springer-Verlag.
[48]M. I. A. Lourakis, A. A. Argyros. SBA:A software package for generic sparse bundle adjustment. ACM Trans. Math. Softw.2009.36(1).1-30
[49]D. Bradley, T. Boubekeur, W. Heidrich. Accurate multi-view reconstruction using robust binocular stereo and surface meshing. Computer Vision and Pattern Recognition,2008. CVPR 2008. IEEE Conference on.2008.1-8
[50]L. Yebin, D. Qionghai, X. Wenli. A Point-Cloud-Based Multiview Stereo Algorithm for Free-Viewpoint Video. Visualization and Computer Graphics, IEEE Transactions on.2010. 16(3).407-418
[51]R. Tylecek, R. Sara. Refinement of Surface Mesh for Accurate Multi-View Reconstruction. Modeling-3D workshop. Proc. of ACCV.2009.
[52]A. Zaharescu, E. Boyer, R. Horaud. TransforMesh:a topology-adaptive mesh-based approach to surface evolution. ACCV.2007.
[53]Y. Furukawa, J. Ponce. Accurate, Dense, and Robust Multiview Stereopsis. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2010.32(8).1362-1376
[54]S. M. Seitz, C. R. Dyer. Photorealistic scene reconstruction by voxel coloring. Computer Vision and Pattern Recognition,1997. Proceedings..1997 IEEE Computer Society Conference on. 1997.1067-1073
[55]K. N. Kutulakos, S. M. Seitz. A theory of shape by space carving. Computer Vision,1999. The Proceedings of the Seventh IEEE International Conference on.1999.307-314 vol.1
[56]G. Vogiatzis, P. H. S. Torr, R. Cipolla. Multi-view stereo via volumetric graph-cuts. Computer Vision and Pattern Recognition,2005. CVPR 2005. IEEE Computer Society Conference on. 2005.391-398 vol.2
[57]K. Kolev, M. Klodt, T. Brox, D. Cremers. Continuous Global Optimization in Multiview 3D Reconstruction. International Journal of Computer Vision.2009.84(1).80-96
[58]M. Lhuillier, L. Quan. A quasi-dense approach to surface reconstruction from uncalibrated images. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2005.27(3). 418-433
[59]C. Hernandez Esteban, F. Schmitt. Silhouette and stereo fusion for 3D object modeling. Computer Vision and Image Understanding.2004.96(3).367-392
[60]K.-J. Yoon, E. Prados, P. Sturm. Joint Estimation of Shape and Reflectance using Multiple Images with Known Illumination Conditions. International Journal of Computer Vision.2010. 86(2).192-210
[61]S. Corazza, L. Mundermann, E. Gambaretto, G. Ferrigno, T. Andriacchi. Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation. International Journal of Computer Vision.2010.87(1).156-169
[62]A. Laurentini. The visual hull concept for silhouette-based image understanding. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1994.16(2).150-162
[63]Y. Wexler, R. Chellappa. View synthesis using convex and visual Hulls. BMVC.2001.
[64]W. N. Martin, J. K. Aggarwal. Volumetric Descriptions of Objects from Multiple Views. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1983. PAMI-5(2).150-158
[65]S. Bo, Q. Xianjie, W. Zhaoqi. Hardware-based camera calibration and 3D modelling under circular motion. Computer Vision and Pattern Recognition Workshops,2008. CVPRW '08. IEEE Computer Society Conference on.2008.1-6
[66]M. Wojciech, B. Chris, R. Ramesh, J. G. Steven, M. Leonard. Image-based visual hulls. Proceedings of the 27th annual conference on Computer graphics and interactive techniques. 2000. ACM Press/Addison-Wesley Publishing Co.
[67]J. S. Franco, E. Boyer. Efficient Polyhedral Modeling from Silhouettes. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2009.31(3).414-427
[68]S. Lazebnik, Y. Furukawa, J. Ponce. Projective Visual Hulls. International Journal of Computer Vision.2007.74(2).137-165
[69]K. Y. K. Wong, R. Cipolla. Structure and motion from silhouettes. Computer Vision,2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on.2001.217-222 vol.2
[70]S. N. Sinha, M. Pollefeys, L. McMillan. Camera network calibration from dynamic silhouettes. Computer Vision and Pattern Recognition,2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on.2004. I-195-1-202 Vol.1
[71]S. Tran, L. Davis.3D Surface Reconstruction Using Graph Cuts with Surface Constraints ECCV. 2006.
[72]R. Hartley, A. Zisserman.韦穗,杨尚骏,章权兵,胡茂林等译.计算机视觉中的多视图几何.合肥.安徽大学出版社.2002
[73]吴福朝.计算机视觉中的数学方法.科学出版社.2008
[74]J. G. Semple, G. T. Kneebone. Algebraic Projective Geometry. Oxford University Press.1998
[75]梅向明,刘增贤,门树慧.高等几何.高等教育出版社.1988
[76]K.B.Atkinson. Close Range Photogrammetry and Machine Vision. Scotland. Whittles Publishing 1996
[77]B. S. R. Armstrong, J. A. Gutierrez. Precision Landmark Location for Machine Vision and Photogrammetry:Finding and Achieving the Maximum Possible Accuracy London. Springer. 2008
[78]R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press.2004
[79]C. B. Owen, X. Fan. P. Middlin. What is the best fiducial? Augmented Reality Toolkit, The First IEEE International Workshop.2002.8 pp.
[80]C. B. Bose, J. Amir. Design of fiducials for accurate registration using machine vision. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1990.12(12).1196-1200
[81]T. A. Clarke. An analysis of the properties of targets used in digital close range photogrammetric measurement. Videometrics III. SPIE Vol.2350.1994.
[82]D. Scharstein, A. J. Briggs. Real-time recognition of self-similar landmarks. Image and Vision Computing.2001.19(11).763-772
[83]Y. Cho, J. Lee, U. Neumann. A Multi-ring Color Fiducial System and an Intensity-invariant Detection Method for Scalable Fiducial-Tracking Augmented Reality Proceedings of the 1st Intl. Workshop on Augmented Reality. San Francisco.1998.
[84]S. J. Ahn, W. Rauh, M. Recknagel. Circular coded landmark for optical 3D-measurement and robot vision. Intelligent Robots and Systems,1999. IROS '99. Proceedings.1999 IEEE/RSJ International Conference on.1999.1128-1133 vol.2
[85]L. Naimark, E. Foxlin. Circular data matrix fiducial system and robust image processing for a wearable vision-inertial self-tracker. Mixed and Augmented Reality,2002. ISMAR 2002. Proceedings. International Symposium on.2002.27-36
[86]M. Fiala. ARTag, a fiducial marker system using digital techniques. Computer Vision and Pattern Recognition,2005. CVPR 2005. IEEE Computer Society Conference on.2005. 590-596 vol.2
[87]C.-T. Schneider. Mehrbildzuordnung. Dimension, Optische Formerfassung. DGZfP. Berlin. 1991.61-67
[88]PhotoModeler. http://www.photomodeler.com/.
[89]AICON. http://www.aicon.de/.
[90]3DM-FotoMetrics. http://www.3dmedia.co.jp/.
[91]张德沟,梁晋,唐正宗,郭成,曹巨明,王欢.大型复杂曲而产品近景工业摄影测量系统开发.光电工程.2009.36(5).7
[92]G. Klein. Visual Tracking for Augmented RealityUniversity of Cambridge.2006
[93]L. Diego. I. pez de, R. S. M. Paulo, H. Andy. TRIP:A Low-Cost Vision-Based Location System for Ubiquitous Computing. Personal Ubiquitous Comput.2002.6(3).206-219
[94]W. Pierre. Adaptive thresholding on the DigitalDesk. EPC-93-110. EuroPARC Technical Report 1993
[95]S. L. Jae. Two-dimensional signal and image processing. Prentice-Hall, Inc.1990
[96]J. Guang, Q. Long. Detection of concentric circles for camera calibration. Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on.2005.333-340 Vol.1
[97]D. Claus, A. W. Fitzgibbon. Reliable Fiducial Detection in Natural Scenes. Computer Vision-ECCV 2004.2004.469-480
[98]马马扬飚,钟约先,郑聆,袁朝龙.三维数据拼接中编码标志点的设计与检测.清华大学学报(自然科学版).2006.46(2).4
[99]K. Forbes, A. Voigt, N. Bodika. An Inexpensive, Automatic and Accurate Camera Calibration Method. Proceedings of the Thirteenth Annual Symposium of the Pattern Recognition Association of South Africa (PRASA 2002).2002.
[100]周玲,张丽艳,郑建冬,张维中.近景摄影测量中标记点的自动检测.应用科学学报.2007.25(3).7
[101]R.-B. Xia, J.-B. Zhao, W.-J. Liu, J.-H. Wu, S.-P. Fu, J. Jiang, J.-Z. Li. A Robust Recognition Algorithm for Encoded Targets in Close-range Photogrammetry. Journal of Information Science and Engineering.2011.27(6).197-209
[102]张义力,吴家升,王军杰.结合COMET与AICON 3D Studio的数据获取方法在逆向工程中的应用研究.机械.2005.32(6).
[103]P. V. Hough. Machine analysis of bubble chamber pictures. International Conference on High Energy Accelerators and Instrumentation.1959.554-556
[104]O. D. Richard, E. H. Peter. Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM.1972.15(1).11-15
[105]D. H. Ballard. Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition.1981.13(2).
[106]S. Maybank. Application of the Fisher-Rao Metric to Ellipse Detection. International Journal of Computer Vision.2007.72(3).287-307
[107]F. Mai, Y. S. Hung, H. Zhong, W. F. Sze. A hierarchical approach for fast and robust ellipse extraction. Pattern Recognition.2008.41(8).2512-2524
[108]N. Otsu. A Threshold Selection Method from Gray-Level Histograms. Systems, Man and Cybernetics, IEEE Transactions on.1979.9(1).62-66
[109]阮秋琦.数字图像处理学.2nd.北京.电子工业出版社.2007
[110]S. Robert. Algorithms in C. Addison-Wesley Longman Publishing Co., Inc.1990
[111]A. Hornberg. Handbook of machine vision. Weinheim Wiley-VCH 2006
[112]F. L. Bookstein. Fitting Conic Sections to Scattered Data. Computer Graphics and Image Processing.1979.9(56-71
[113]A. Sung Joon. Least Squares Orthogonal Distance Fitting of Curves and Surfaces in Space (Lecture Notes in Computer Science). Springer-Verlag New York, Inc.2004
[114]K. Kanatani. Statistical optimization for geometric computation:theory and practice. Elsevier. 1996
[115]A. W. Fitzgibbon, M. Pilu, R. B. Fisher. Direct least squares fitting of ellipses. Pattern Recognition,1996., Proceedings of the 13th International Conference on.1996.253-257 vol.1
[116]K. Kanatani. Statistical bias of conic fitting and renormalization. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1994.16(3).320-326
[117]K. Voss, H. Suesse. A new one-parametric fitting method for planar objects. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1999.21 (7).646-651
[118]B. Matei, P. Meer. A general method for Errors-in-Variables problems in computer vision. Computer Vision and Pattern Recognition,2000. Proceedings. IEEE Conference on.2000. 18-25 vol.2
[119]K. Kanatani. Geometric computation for machine vision Oxford University Press.1993
[120]Ellipse. http://mathworld.wolfram.com/Ellipse.html
[121]V. Fremont, R. Chellali. Direct camera calibration using two concentric circles from a single view, lnt Conf Artif Real Telexistence 2002.93-98
[122]J. Matas, O. Chum, M. Urban, T. Pajdla. Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing.2004.22(10).761-767
[123]R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision.2nd. Cambridge University Press.2003
[124]S. Bougnoux. From projective to Euclidean space under any practical situation, a criticism of self-calibration. The Sixth International Conference on Computer Vision.1998.790-796
[125]G. Zhang, X. Qin, W. Hua, T.-T. Wong, P.-A. Heng, H. Bao. Robust Metric Reconstruction from Challenging Video Sequences. IEEE Conference on Computer Vision and Pattern Recognition.2007.
[126]N. Snavely, S. M. Seitz, R. Szeliski. Photo Tourism:Exploring Photo Collections in 3D. ACM SIGGRAPH. Boston, Massachusetts.2006. ACM.
[127]P. Sturm, Z. L. Cheng, P. C. Y. Chen, A. N. Poo. Focal length calibration from two views: method and analysis of singular cases. Computer Vision and Image Understanding.2005. 99(1).58-95
[128]Y.-j. Zhou, X.-j. Kou. A practical iterative two-view metric reconstruction with uncalibrated cameras. Journal of Zhejiang University-Science A.2007.8(10).1614-1623
[129]K. Kanatani, C. Matsunaga. Closed-Form Expression for Focal Lengths from the Fundamental Matrix. Proc.4th Asian Conf. Computer Vision.2000.128-133
[130]T. Ueshiba, F. Tomita. Self-calibration from two perspective views under various conditions: Closed-form solutions and degenerate configurations. Proc. Australia-Japan Advanced Workshop on Computer Vision.2003.118-125
[131]K. Kanatani, A. Nakatsuji, Y. Sugaya. Stabilizing the Focal Length Computation for 3-D Reconstruction from Two Uncalibrated Views. International Journal of Computer Vision.2006. 66(2).109-122
[132]H. Stewenius. D. Nister, F. Kahl. F. Schaffalitzky. A Minimal Solution for Relative Pose with Unknown Focal Length. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2005. IEEE Computer Society.
[133]Z. Kukelova, M. Bujnak, T. Pajdla. Automatic Generator of Minimal Problem Solvers. Computer Vision - ECCV 2008.2008. Springer Berlin/Heidelberg.302-315
[134]M. Byrod. K. Josephson. K. Astrom. Improving Numerical Accuracy of Grobner Basis Polynomial Equation Solvers. Computer Vision,2007.ICCV 2007. IEEE 11th International Conference on.2007.1-8
[135]H. Li. A simple solution to the six-point two-view focallength problem. European Conference on Computer Vision.2006.200-213
[136]B. Bocquillon, A. Bartoli, P. Gurdjos, A. Crouzil. On Constant Focal Length Self-Calibration From Multiple Views. IEEE Conference on Computer Vision and Pattern Recognition.2007. 1-8
[137]B. Triggs. Autocalibration and the absolute quadric. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.1997.609-614
[138]D. Nister. Untwisting a Projective Reconstruction. Int. J. Comput. Vision.2004.60(2). 165-183
[139]M. Chandraker, S. Agarwal, F. Kahl, D. Nister, D. Kriegman. Autocalibration via Rank-Constrained Estimation of the Absolute Quadric. IEEE Conference on Computer Vision and Pattern Recognition.2007.1-8
[140]J. B. Lasserre. Global Optimization with Polynomials and the Problem of Moments. SIAM J. on Optimization.2001.11 (3).796-817
[141]F. Kahl, D. Henrion. Globally optimal estimates for geometric reconstruction problems. Tenth IEEE International Conference on Computer Vision.2005.978-985
[142]A. Habed, B. Boufama. Camera self-calibration from bivariate polynomials derived from Kruppa's equations. Pattern Recogn.2008.41(8).2484-2492
[143]雷成,胡占义,吴福朝,H.T.TSUI.一种新的基于Kruppa方程的摄像机自标定方法.计算机学报2003.26(5).
[144]D. Roble. Vision in film and special effects. SIGGRAPH Comput. Graph.1999.33(4).58-60
[145]K. Cornelis, M. Pollefeys, L. Van Gool. Tracking based structure and motion recovery for augmented video productions. ACM Symposium on Virtual Reality Software and Technology. 2001.17-24
[146]H. Anton van den, D. Anthony, T. Thorsten, hlen, W. Ben, H. S. T. Philip. VideoTrace:rapid interactive scene modelling from video. ACM SIGGRAPH 2007 papers. San Diego. California.2007. ACM.
[147]J. Oliensis. A Critique of Structure-from-Motion Algorithms. Computer Vision and Image Understanding.2000.80(2).172-214
[148]C. Zeller, O. Faugeras. Camera Self-Calibration from Video Sequences:the Kruppa Equations Revisited. RR-2793. INRIA.1996
[149]M. Pollefeys, L. van Gool. Stratified self-calibration with the modulus constraint. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1999.21(8).707-724
[150]L. Maxime, Q. Long. Quasi-Dense Reconstruction from Image Sequence. Proceedings of the 7th European Conference on Computer Vision-Part Ⅱ.2002. Springer-Verlag.
[151]M. Pollefeys, F. Verbiest, L. Van Gool. Surviving Dominant Planes in Uncalibrated Structure and Motion Recovery. Computer Vision — ECCV 2002.2002.613-614
[152]J. Repko, M. Pollefeys.3D models from extended uncalibrated video sequences:addressing key-frame selection and projective drift. Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling.2005.150-157
[153]S. Jianbo, C. Tomasi. Good features to track. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.1994.593-600
[154]D. Chen, G. Zhang. A New Sub-Pixel Detector for X-Corners in Camera Calibration Targets. WSCG Short Papers.2005.97-100
[155]L. Matthews, T. Ishikawa, S. Baker. The template update problem. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2004.26(6).810-815
[156]S. Gibson, J. Cook, T. Howard, R. A. H. R. Hubbold, D. A. O. D. Oram. Accurate camera calibration for off-line, video-based augmented reality. Proceedings of the International Symposium on Mixed and Augmented Reality (ISMAR'02).2002.37-46
[157]P. H. S. Torr, A. Zisserman. MLESAC:a new robust estimator with application to estimating image geometry. Comput. Vis. Image Underst.2000.78(1).138-156
[158]F. Schaffalitzky. A. Zisserman, R. Hartley, P. Torr. A Six Point Solution for Structure and Motion. Computer Vision - ECCV 2000.2000.632-648
[159]D. Nister. Reconstruction from Uncalibrated Sequences with a Hierarchy of Trifocal Tensors. Computer Vision - ECCV 2000.2000.649-663
[160]M. Vergauwen, L. Van Gool. Web-based 3D Reconstruction Service. Machine Vision and Applications.2006.17(6).411-426
[161]M. Byrod, K. Josephson, K. Astrom. Fast Optimal Three View Triangulation. Computer Vision-ACCV 2007.2007.549-559
[162]R. I. Hartley, P. Sturm. Triangulation. Comput. Vis. Image Underst.1997.68(2).146-157
[163]H. S. Sawhney, Y. Guo, J. Asmuth, R. Kumar. Multi-view 3D estimation and applications to match move. Multi-View Modeling and Analysis of Visual Scenes,1999. (MVIEW '99) Proceedings. IEEE Workshop on.1999.21-28
[164]S. J. Sheather, M. C. Jones. A Reliable Data-Based Bandwidth Selection Method for Kernel Density Estimation. Journal of the Royal Statistical Society. Series B (Methodological).1991. 53(3).683-690
[165]J. Nocedal, S. Wright. Numerical Optimization. New York. Springer.2000
[166]B. K. P. Horn, H. M. Hilden. S. Negahdaripour. Closed-form solution of absolute orientation using orthonormal matrices. J. Opt. Soc.Am. A.1988.5(7).1127-1135
[167]C. Olsson, F. Kahl, M. Oskarsson. The Registration Problem Revisited:Optimal Solutions From Points, Lines and Planes. Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).2006.1206-1213
[168]J. Yekeun, K. In-So. Relative scale estimation between two camera motions. Pattern Recognition,2008. ICPR 2008.19th International Conference on.2008.1-4
[169]I. Esteban, L. Dorst, J. Dijk. Closed Form Solution for the Scale Ambiguity Problem in Monocular Visual Odometry. Intelligent Robotics and Applications. Springer Berlin/ Heidelberg.2010.665-679
[170]F. Fraundorfer, D. Scaramuzza, M. Pollefeys. A Constricted Bundle Adjustment Parameterization for Relative Scale Estimation in Visual Odometry. IEEE International Conference on Robotics and Automation.2010.
[171]R. Hartley, F. Schaffalitzky. L_infin minimization in geometric reconstruction problems. Proceedings of the Conference on Computer Vision and Pattern Recognition.2004. 1-504-1-509 Vol.1
[172]F. Kahl, R. Hartley. Multiple-View Geometry Under the L-infinity-Norm. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2008.30(9).1603-1617
[173]M. S. Lobo, L. Vandenberghe, S. Boyd, H. Lebret. Applications of second-order cone programming. Linear Algebra and its Applications.1998.284(1-3).193-228
[174]D. Martinec, T. Pajdla.3D Reconstruction by Gluing Pair-Wise Euclidean Reconstructions, or "How to Achieve a Good Reconstruction from Bad Images".3D Data Processing, Visualization, and Transmission, Third International Symposium on.2006.25-32
[175]J. F. Sturm. Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones. Optimization Methods and Software.1999.11(1).625-653
[176]I. E. Sutherland. Three-dimensional data input by tablet. Proceedings of the IEEE.1974.62(4). 453-461
[177]R. M. Haralick, L. G. Shapiro. Computer and Robot Vision. Prentice Hall 1993
[178]P. A. Beardsley, P. H. S. Torr, A. Zisserman.3D Model Acquisition from Extended Image Sequences. Proceedings of the 4th European Conference on Computer Vision-Volume Ⅱ-Volume Ⅱ.1996. Springer-Verlag.
[179]Community Photo Collections:http://grail.cs.washington.edu/projects/cpc/.
[180]F. Schaffalitzky, A. Zisserman. Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?". Computer Vision — ECCV 2002.2002.414-431
[181]J. Yao, W.-K. Cham. Robust multi-view feature matching from multiple unordered views. Pattern Recognition.2007.40(11).3081-3099
[182]X. Zeng, Q. Wang, J. Xu. MAP Model for Large-scale 3D Reconstruction and Coarse Matching for Unordered Wide-baseline Photos. BMVC.2008.
[183]R. Gherardi, M. Farenzena, A. Fusiello. Improving the efficiency of hierarchical structure-and-motion. Computer Vision and Pattern Recognition (CVPR),2010 IEEE Conference on.2010.1594-1600
[184]M. Brown, D. G. Lowe. Unsupervised 3D object recognition and reconstruction in unordered datasets.3-D Digital Imaging and Modeling,2005.3DIM 2005. Fifth International Conference on.2005.56-63
[185]N. Snavely, S. M. Seitz, R. Szeliski. Skeletal graphs for efficient structure from motion. Computer Vision and Pattern Recognition,2008. CVPR 2008. IEEE Conference on.2008.1-8
[186]C. Olsson, O. Enqvist. Stable Structure from Motion for Unordered Image Collections. Image Analysis. Springer Berlin/Heidelberg.2011.524-535
[187]T. Werner, T. Pajdla, M. Urban. Practice of 3D Reconstruction from Multiple Uncalibrated Unorganized Images. Proceedings of the Czech Pattern Recognition Workshop.2000.
[188]D. Martinec, T. Pajdla.3D reconstruction by fitting low-rank matrices with missing data. Computer Vision and Pattern Recognition,2005. CVPR 2005. IEEE Computer Society Conference on.2005.198-205 vol.1
[189]H. Cornelius, R. Sara, D. Martinec, T. Pajdla, O. Chum, J. Matas. Towards Complete Free-Form Reconstruction of Complex 3D Scenes from an Unordered Set of Uncalibrated Images. ECCV Workshop SMVP.2004.1-12
[190]A. Heyden. Projective Structure and Motion from Image Sequences using Subspace Methods. Proc.10th Scandinavian Conference on Image Analysis.1997.963-968
[191]M. Havlena, A. Torii, J. Knopp, T. Pajdla. Randomized Structure from Motion Based on Atomic 3D Models from Camera Triplets. Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2009.2874-2881
[192]M. Bujnak, Z. Kukelova, T. Pajdla.3D reconstruction from image collections with a single known focal length. Computer Vision,2009 IEEE 12th International Conference on.2009. 1803-1810
[193]D. Martinec, T. Pajdla. Robust Rotation and Translation Estimation in Multiview Reconstruction. Computer Vision and Pattern Recognition,2007. CVPR '07. IEEE Conference on.2007.1-8
[194]R. Datta, D. Joshi. J. Li, J. Z. Wang. Image retrieval:Ideas, influences, and trends of the new age. ACM Comput. Surv.2008.40(2).1-60
[195]C. Harris, M. Stephens. A Combined Corner and Edge Detector. Proceedings of the Fourth Alvev Vision Conference.1988.147-152
[196]K. Mikolajczyk, C. Schmid. Indexing based on scale invariant interest points. Computer Vision.2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on.2001. 525-531 vol.1
[197]P. R. Beaudet. Rotationally invariant image operators. Proceedings of the 4th International Joint Conference on Pattern Recognition. Tokyo.1978.579-583
[198]K. Mikolajczyk, C. Schmid. A Performance Evaluation of Local Descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2005.27(10).1615-1630
[199]S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, A. Y. Wu. An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. ACM.1998.45(6).891-923
[200]P. Torr, A. W. Fitzgibbon, A. Zisserman. Maintaining multiple motion model hypotheses over many views to recover matching and structure. Computer Vision,1998. Sixth International Conference on.1998.485-491
[201]P. H. S. Torr, A. W. Fitzgibbon, A. Zisserman. The Problem of Degeneracy in Structure and Motion Recovery from Uncalibrated Image Sequences. International Journal of Computer Vision.1999.32(1).27-44
[202]J. Joseph B. Kruskal. On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem. Proceedings of the American Mathematical Society.1956.48-50
[203]J.-K. Seo, H.-K. Hong, C.-W. Jho. M.-H. Choi. Two quantitative measures of inlier distributions for precise fundamental matrix estimation. Pattern Recognition Letters.2004. 25(6).733-741
[204]D. Oram. Projective Reconstruction and Metric Models from Uncalibrated Video SequencesPh.D. Degree. Manchester university.2001
[205]M. Urban, T. Pajdla, V. Hlavac. Projective Reconstruction from N Views Having One View in Common. Vision Algorithms:Theory and Practice.2000.116-131
[206]B. Triggs. Factorization methods for projective structure and motion. Computer Vision and Pattern Recognition,1996. Proceedings CVPR'96.1996 IEEE Computer Society Conference on.1996.845-851
[207]P. A. Parrilo. Semidefinite programming relaxations for semialgebraic problems. Mathematical Programming.2003.96(2).293-320
[208]D. Henrion, J.-B. Lasserre. GloptiPoly:Global optimization over polynomials with Matlab and SeDuMi. ACM Trans. Math. Softw.2003.29(2).165-194
[209]C. Strecha, W. v. Hansen, L. V. Gool, P. Fua, U. Thoennessen. On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery. CVPR.2008.
[210]W. Niem. Automatic reconstruction of 3D objects using a mobile camera. Image and Vision Computing.1999.17(2).125-134
[211]F. Schmitt, Y. Yemez.3D color object reconstruction from 2D image sequences. Image Processing,1999. ICIP 99. Proceedings.1999 International Conference on.1999.65-69 vol.3
[212]B. Adam, L. Alex, T. Richard.3D S.O.M.:a commercial software solution to 3D scanning. Graph. Models.2005.67(6).476-495.
[213]M. Pollefeys, L. Van Gool, M. Vergauwen, F. Verbiest, K. Cornelis, J. Tops, R. Koch. Visual Modeling with a Hand-Held Camera. International Journal of Computer Vision.2004.59(3). 207-232
[214]R. Koch, J.-F. Evers-Senne, J.-M. Frahm, K. Koeser.3D Reconstruction and Rendering from Image Sequences WIAMIS.2005.
[215]E. Imre, S. Knorr, B. Ozkalayci, U. Topay, A. Aydin Alatan, T. Sikora. Towards 3-D scene reconstruction from broadcast video. Signal Processing:Image Communication.2007.22(2). 108-126
[216]C.-M. Cheng, S.-F. Wang, C.-H. Teng, S.-H. Lai. Image-based three-dimensional model reconstruction for Chinese treasure--Jadeite Cabbage with Insects. Computers & Graphics. 2008.32(6).682-694
[217]Y. Y. Boykov, M. P. Jolly. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. Computer Vision,2001.1CCV 2001. Proceedings. Eighth IEEE International Conference on.2001.105-112 vol.1
[218]陈京,袁保宗.丛于视觉凸包的真实感模型重建[技术报告].北京交通大学信息科学研究所.2009
[219]W. E. Lorensen, H. E. Cline. Marching cubes:A high resolution 3D surface construction algorithm. SIGGRAPH Comput. Graph.1987.21(4).163-169
[220]陈京,袁保宗.丛于视频的模型重建中帧优化选择方法[技术报告].北京交通大学信息科学研究所.2010
[221]F. Bernardini, J. Mittleman. H. Rushmeier, C. Silva, G. Taubin. The Ball-Pivoting Algorithm for Surface Reconstruction. IEEE Transactions on Visualization and Computer Graphics.1999. 5(4).349-359
[222]M. Garland, P. S. Heckbert. Surface simplification using quadric error metrics. Proceedings of the 24th annual conference on Computer graphics and interactive techniques.1997. ACM Press/Addison-Wesley Publishing Co.
[223]M. Attene, B. Falcidieno, M. Spagnuolo. Hierarchical mesh segmentation based on fitting primitives. The Visual Computer.2006.22(3).181-193
[224]A. Watt, F. Policarpo.3D Games:Animation and Advanced Real-time Rendering, Volume2(3D游戏第二卷动画与高级实时渲染技术).Addison Wesley.2003
[225]S. R. Buss.3D Computer Graphics:A Mathematical Introduction with OpenGL. Cambridge University Press 2003
[226]F. Pighin, J. Hecker, D. Lischinski, R. Szeliski, D. H. Salesin. Synthesizing realistic facial expressions from photographs. Proceedings of the 25th annual conference on Computer graphics and interactive techniques.1998. ACM.
[227]陈京,袁保宗.丛于匹配点的真实感模型重建[技术报告].北京交通大学信息科学研究所.2010
[228]S. M. Seitz, B. Curless, J. Diebel, D. Scharstein, R. Szeliski. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms. Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on.2006.519-528
[229]http://cmp.felk.cvut.cz/projects/is3d/Data.html.
[230]Y. Ma, K. Huang, R. Vidal, Koeck, aacute, J, S. Sastry. Rank Conditions on the Multiple-View Matrix. International Journal of Computer Vision.2004.59(115-137)
[231]C. P. Lu, G. D. Hager. E. Mjolsness. Fast and globally convergent pose estimation from video images. Pattern Analysis and Machine Intelligence. IEEE Transactions on.2000.22(6). 610-622
[232]L. Hinsken. A Singularity Free Algorithm for Spatial Orientation of Bundles. International Archives of Photogrammetry andRemote Sensing. Kyoto, Japan.1988.
[233]E. M. Mikhail, J. S. Bethel, J. C. McGlone. Introduction to Modern Photogrammetry Wiley. 2001
[234]R. I. Hartley. Cheirality invariants. DARPA Image Understanding Workshop.1993.745-753
[2]S. Pastoor, L. Jin, S. Renault. An experimental multimedia system allowing 3-D visualization and eye-controlled interaction without user-worn devices. Multimedia, IEEE Transactions on. 1999.1(1).41-52
[3]M. Aakay, I. Marsic, A. Medl, B. Guangming. A system for medical consultation and education using multimodal human/machine communication. Information Technology in Biomedicine, IEEE Transactions on.1998.2(4).282-291
[4]A. Cheyer, L. Julia. Multimodal Maps:An Agent-based Approach. International Conference on Cooperative Multimodal Communication (CMC/95). Eindhoven, The Netherlands.1995.
[5]N. Magnenat-Thalmann, S. Kshirsagar. Communicating with Autonomous Virtual Humans. Proc. of the 17th TWENTE Workshop on Language TechnologyEaschede. Universiteit Twente. 2000.1-8
[6]莫庆远.超级智能视听信息处理系统环境的研究[博士学位论文].信息科学研究所,北方交通大学.1992
[7]M. Zhenjiang, J.-L. Basille, B. Yuan. OOPR:A Novel Theory for Multimedia Information Processing. Proc. of Sixth International Conference on Signal Processing Applications and Technology. Boston, USA.1995.
[8]阮秋琦,袁保宗.智能视听信息处理的新策略.北方交通大学学报.1996.20(2).152-160
[9]高文,袁保宗.多功能感知机.国家自然科学丛金重点项目申请书(内部报告).1998
[10]袁保宗,阮秋琦,王延江,刘汝杰,唐晓芳.新一代(第四代)人机交互的概念框架特征及关键技术.电子学报.2003.31(12A).1945-1954
[11]王延江.新一代(第四代)人机交互的概念框架及其关键技术研究[博士学位论文].北方交通大学.2001
[12]孟祥旭,李学庆.人机交互技术：原理与应用.北京.清华大学出版社.2004
[13]邓世伟.基于CV/CAD的三维物体AVR建模[博士学位论文].信息科学研究所,北方交通大学.1994
[14]B. Yuan, C. Zhang, X. Tang. AVR Theory and Applications:Virtualizing the Real World Objects. JCSSP. Tokyo.1997.103-108
[15]刘渭滨.从真实世界到虚拟现实：AVR与虚拟环境建模的研究与实现[博士学位论文].信息科学研究所,北方交通大学.2001
[16]胡明星.未定标系统下几何估计、摄像机定标与三维重建研究[博士学位论文].计算机学院,北京交通大学.2003
[17]张超.三维曲面物体的计算机视觉表示、造型和重建的研究与实现[博上学位论文].信息科学研究所,北方交通大学.1994
[18]邢薇薇.AVR与三维物体识别的研究与实现[博上学位论文].计算机学院,北京交通大学.2006
[19]Y. I. Abdel-Aziz, H. M. Karara. Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. Proceedings of the Symposium on Close-Range Photogrammetry.1971. American Society of Photogrammetry.1-18
[20]Z. Zhang. A flexible new technique for camera calibration. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2000.22(11).1330-1334
[21]Y. Wu, X. Li, F. Wu, Z. Hu. Coplanar circles, quasi-affine invariance and calibration. Image and Vision Computing.2006.24(4).319-326
[22]Y. Xianghua, Z. Hongbin. A Novel Linear Approach to Camera Calibration from Sphere Images. Pattern Recognition,2006. ICPR 2006.18th International Conference on.2006.535-538
[23]Z. Zhang. Camera calibration with one-dimensional objects. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2004.26(7).892-899
[24]王亮,吴福朝.基于一维标定物的多摄像机标定.自动化学报.2007.33(3).
[25]P. Torr, A. Zisserman. Feature Based Methods for Structure and Motion Estimation. Vision Algorithms:Theory and Practice.2000.278-294
[26]D. G. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision.2004.60(2).91-110
[27]S. Jianbo. C. Tomasi. Good features to track. Computer Vision and Pattern Recognition,1994. Proceedings CVPR'94.,1994 IEEE Computer Society Conference on.1994.593-600
[28]J. Canny. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 1986.8(6).679-698
[29]C. Schmid, A. Zisserman. Automatic line matching across views. Computer Vision and Pattern Recognition,1997. Proceedings.,1997 IEEE Computer Society Conference on.1997.666-671
[30]B. Lucas, T. Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. Proc. of 7th International Joint Conference on Artificial Intelligence (IJCAI).1981.
[31]M. A. Fischler, R. C. Bolles. Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM.1981.24(6). 381-395
[32]R. I. Hartley. In defense of the eight-point algorithm. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1997.19(6).580-593
[33]Z. Zhang, D. R., F. O., Q.-T. L. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry Artificial Intelligence.1995.78(1). 87-119
[34]Q.-T. Luong, O. D. Faugeras. The fundamental matrix:Theory, algorithms, and stability analysis. International Journal of Computer Vision.1996.17(1).43-75
[35]L. Quan. Invariants of six points and projective reconstruction from three uncalibrated images. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1995.17(1).34-46
[36]P. H. S. Torr, A. Zisserman. Robust parameterization and computation of the trifocal tensor. Image and Vision Computing.1997.15(8).591-605
[37]R. Szeliski. Computer Vision:Algorithms and Applications. Springer.2010
[38]P. A. Beardsley, A. Zisserman, D. W. Murray. Sequential Updating of Projective and Affine Structure from Motion. International Journal of Computer Vision.1997.23(3).235-259
[39]P. Sturm, B. Triggs. A factorization based algorithm for multi-image projective structure and motion. Computer Vision — ECCV '96.1996.709-720
[40]S. Christy, R. Horaud. Euclidean Shape and Motion from Multiple Perspective Views by Affine Iterations. IEEE Trans. Pattern Anal. Mach. Intell.1996.18(11).1098-1104
[41]F. Kahl. Multiple View Geometry and the L-infinity Norm. Computer Vision,2005.ICCV 2005. Tenth IEEE International Conference on.2005.1002-1009 Vol.2
[42]A. W. Fitzgibbon, A. Zisserman. Automatic Camera Recovery for Closed or Open Image Sequences. Computer Vision — ECCV '98.1998.311
[43]H.-Y. Shum, Q. Ke, Z. Zhang. Efficient bundle adjustment with virtual key frames:a hierarchical approach to multi-frame structure from motion. Computer Vision and Pattern Recognition,1999. IEEE Computer Society Conference on.1999.543 Vol.2
[44]O. D. Faugeras, Q.-T. Luong, S. J. Maybank. Camera Self-Calibration:Theory and Experiments. Proceedings of the Second European Conference on Computer Vision.1992. Springer-Verlag.
[45]L. Agapito, E. Hayman, I. Reid. Self-Calibration of Rotating and Zooming Cameras. International Journal of Computer Vision.2002.47(1).287-287
[46]M. Pollefeys, R. Koch, L. V. Gool. Self-Calibration and Metric Reconstruction Inspite of Varying and Unknown Intrinsic Camera Parameters. International Journal of Computer Vision. 1999.32(1).7-25
[47]B. Triggs, F. M. Philip, I. H. Richard, W. F. Andrew. Bundle Adjustment - A Modern Synthesis. Proceedings of the International Workshop on Vision Algorithms:Theory and Practice.2000. Springer-Verlag.
[48]M. I. A. Lourakis, A. A. Argyros. SBA:A software package for generic sparse bundle adjustment. ACM Trans. Math. Softw.2009.36(1).1-30
[49]D. Bradley, T. Boubekeur, W. Heidrich. Accurate multi-view reconstruction using robust binocular stereo and surface meshing. Computer Vision and Pattern Recognition,2008. CVPR 2008. IEEE Conference on.2008.1-8
[50]L. Yebin, D. Qionghai, X. Wenli. A Point-Cloud-Based Multiview Stereo Algorithm for Free-Viewpoint Video. Visualization and Computer Graphics, IEEE Transactions on.2010. 16(3).407-418
[51]R. Tylecek, R. Sara. Refinement of Surface Mesh for Accurate Multi-View Reconstruction. Modeling-3D workshop. Proc. of ACCV.2009.
[52]A. Zaharescu, E. Boyer, R. Horaud. TransforMesh:a topology-adaptive mesh-based approach to surface evolution. ACCV.2007.
[53]Y. Furukawa, J. Ponce. Accurate, Dense, and Robust Multiview Stereopsis. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2010.32(8).1362-1376
[54]S. M. Seitz, C. R. Dyer. Photorealistic scene reconstruction by voxel coloring. Computer Vision and Pattern Recognition,1997. Proceedings..1997 IEEE Computer Society Conference on. 1997.1067-1073
[55]K. N. Kutulakos, S. M. Seitz. A theory of shape by space carving. Computer Vision,1999. The Proceedings of the Seventh IEEE International Conference on.1999.307-314 vol.1
[56]G. Vogiatzis, P. H. S. Torr, R. Cipolla. Multi-view stereo via volumetric graph-cuts. Computer Vision and Pattern Recognition,2005. CVPR 2005. IEEE Computer Society Conference on. 2005.391-398 vol.2
[57]K. Kolev, M. Klodt, T. Brox, D. Cremers. Continuous Global Optimization in Multiview 3D Reconstruction. International Journal of Computer Vision.2009.84(1).80-96
[58]M. Lhuillier, L. Quan. A quasi-dense approach to surface reconstruction from uncalibrated images. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2005.27(3). 418-433
[59]C. Hernandez Esteban, F. Schmitt. Silhouette and stereo fusion for 3D object modeling. Computer Vision and Image Understanding.2004.96(3).367-392
[60]K.-J. Yoon, E. Prados, P. Sturm. Joint Estimation of Shape and Reflectance using Multiple Images with Known Illumination Conditions. International Journal of Computer Vision.2010. 86(2).192-210
[61]S. Corazza, L. Mundermann, E. Gambaretto, G. Ferrigno, T. Andriacchi. Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation. International Journal of Computer Vision.2010.87(1).156-169
[62]A. Laurentini. The visual hull concept for silhouette-based image understanding. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1994.16(2).150-162
[63]Y. Wexler, R. Chellappa. View synthesis using convex and visual Hulls. BMVC.2001.
[64]W. N. Martin, J. K. Aggarwal. Volumetric Descriptions of Objects from Multiple Views. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1983. PAMI-5(2).150-158
[65]S. Bo, Q. Xianjie, W. Zhaoqi. Hardware-based camera calibration and 3D modelling under circular motion. Computer Vision and Pattern Recognition Workshops,2008. CVPRW '08. IEEE Computer Society Conference on.2008.1-6
[66]M. Wojciech, B. Chris, R. Ramesh, J. G. Steven, M. Leonard. Image-based visual hulls. Proceedings of the 27th annual conference on Computer graphics and interactive techniques. 2000. ACM Press/Addison-Wesley Publishing Co.
[67]J. S. Franco, E. Boyer. Efficient Polyhedral Modeling from Silhouettes. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2009.31(3).414-427
[68]S. Lazebnik, Y. Furukawa, J. Ponce. Projective Visual Hulls. International Journal of Computer Vision.2007.74(2).137-165
[69]K. Y. K. Wong, R. Cipolla. Structure and motion from silhouettes. Computer Vision,2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on.2001.217-222 vol.2
[70]S. N. Sinha, M. Pollefeys, L. McMillan. Camera network calibration from dynamic silhouettes. Computer Vision and Pattern Recognition,2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on.2004. I-195-1-202 Vol.1
[71]S. Tran, L. Davis.3D Surface Reconstruction Using Graph Cuts with Surface Constraints ECCV. 2006.
[72]R. Hartley, A. Zisserman.韦穗,杨尚骏,章权兵,胡茂林等译.计算机视觉中的多视图几何.合肥.安徽大学出版社.2002
[73]吴福朝.计算机视觉中的数学方法.科学出版社.2008
[74]J. G. Semple, G. T. Kneebone. Algebraic Projective Geometry. Oxford University Press.1998
[75]梅向明,刘增贤,门树慧.高等几何.高等教育出版社.1988
[76]K.B.Atkinson. Close Range Photogrammetry and Machine Vision. Scotland. Whittles Publishing 1996
[77]B. S. R. Armstrong, J. A. Gutierrez. Precision Landmark Location for Machine Vision and Photogrammetry:Finding and Achieving the Maximum Possible Accuracy London. Springer. 2008
[78]R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press.2004
[79]C. B. Owen, X. Fan. P. Middlin. What is the best fiducial? Augmented Reality Toolkit, The First IEEE International Workshop.2002.8 pp.
[80]C. B. Bose, J. Amir. Design of fiducials for accurate registration using machine vision. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1990.12(12).1196-1200
[81]T. A. Clarke. An analysis of the properties of targets used in digital close range photogrammetric measurement. Videometrics III. SPIE Vol.2350.1994.
[82]D. Scharstein, A. J. Briggs. Real-time recognition of self-similar landmarks. Image and Vision Computing.2001.19(11).763-772
[83]Y. Cho, J. Lee, U. Neumann. A Multi-ring Color Fiducial System and an Intensity-invariant Detection Method for Scalable Fiducial-Tracking Augmented Reality Proceedings of the 1st Intl. Workshop on Augmented Reality. San Francisco.1998.
[84]S. J. Ahn, W. Rauh, M. Recknagel. Circular coded landmark for optical 3D-measurement and robot vision. Intelligent Robots and Systems,1999. IROS '99. Proceedings.1999 IEEE/RSJ International Conference on.1999.1128-1133 vol.2
[85]L. Naimark, E. Foxlin. Circular data matrix fiducial system and robust image processing for a wearable vision-inertial self-tracker. Mixed and Augmented Reality,2002. ISMAR 2002. Proceedings. International Symposium on.2002.27-36
[86]M. Fiala. ARTag, a fiducial marker system using digital techniques. Computer Vision and Pattern Recognition,2005. CVPR 2005. IEEE Computer Society Conference on.2005. 590-596 vol.2
[87]C.-T. Schneider. Mehrbildzuordnung. Dimension, Optische Formerfassung. DGZfP. Berlin. 1991.61-67
[88]PhotoModeler. http://www.photomodeler.com/.
[89]AICON. http://www.aicon.de/.
[90]3DM-FotoMetrics. http://www.3dmedia.co.jp/.
[91]张德沟,梁晋,唐正宗,郭成,曹巨明,王欢.大型复杂曲而产品近景工业摄影测量系统开发.光电工程.2009.36(5).7
[92]G. Klein. Visual Tracking for Augmented RealityUniversity of Cambridge.2006
[93]L. Diego. I. pez de, R. S. M. Paulo, H. Andy. TRIP:A Low-Cost Vision-Based Location System for Ubiquitous Computing. Personal Ubiquitous Comput.2002.6(3).206-219
[94]W. Pierre. Adaptive thresholding on the DigitalDesk. EPC-93-110. EuroPARC Technical Report 1993
[95]S. L. Jae. Two-dimensional signal and image processing. Prentice-Hall, Inc.1990
[96]J. Guang, Q. Long. Detection of concentric circles for camera calibration. Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on.2005.333-340 Vol.1
[97]D. Claus, A. W. Fitzgibbon. Reliable Fiducial Detection in Natural Scenes. Computer Vision-ECCV 2004.2004.469-480
[98]马马扬飚,钟约先,郑聆,袁朝龙.三维数据拼接中编码标志点的设计与检测.清华大学学报(自然科学版).2006.46(2).4
[99]K. Forbes, A. Voigt, N. Bodika. An Inexpensive, Automatic and Accurate Camera Calibration Method. Proceedings of the Thirteenth Annual Symposium of the Pattern Recognition Association of South Africa (PRASA 2002).2002.
[100]周玲,张丽艳,郑建冬,张维中.近景摄影测量中标记点的自动检测.应用科学学报.2007.25(3).7
[101]R.-B. Xia, J.-B. Zhao, W.-J. Liu, J.-H. Wu, S.-P. Fu, J. Jiang, J.-Z. Li. A Robust Recognition Algorithm for Encoded Targets in Close-range Photogrammetry. Journal of Information Science and Engineering.2011.27(6).197-209
[102]张义力,吴家升,王军杰.结合COMET与AICON 3D Studio的数据获取方法在逆向工程中的应用研究.机械.2005.32(6).
[103]P. V. Hough. Machine analysis of bubble chamber pictures. International Conference on High Energy Accelerators and Instrumentation.1959.554-556
[104]O. D. Richard, E. H. Peter. Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM.1972.15(1).11-15
[105]D. H. Ballard. Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition.1981.13(2).
[106]S. Maybank. Application of the Fisher-Rao Metric to Ellipse Detection. International Journal of Computer Vision.2007.72(3).287-307
[107]F. Mai, Y. S. Hung, H. Zhong, W. F. Sze. A hierarchical approach for fast and robust ellipse extraction. Pattern Recognition.2008.41(8).2512-2524
[108]N. Otsu. A Threshold Selection Method from Gray-Level Histograms. Systems, Man and Cybernetics, IEEE Transactions on.1979.9(1).62-66
[109]阮秋琦.数字图像处理学.2nd.北京.电子工业出版社.2007
[110]S. Robert. Algorithms in C. Addison-Wesley Longman Publishing Co., Inc.1990
[111]A. Hornberg. Handbook of machine vision. Weinheim Wiley-VCH 2006
[112]F. L. Bookstein. Fitting Conic Sections to Scattered Data. Computer Graphics and Image Processing.1979.9(56-71
[113]A. Sung Joon. Least Squares Orthogonal Distance Fitting of Curves and Surfaces in Space (Lecture Notes in Computer Science). Springer-Verlag New York, Inc.2004
[114]K. Kanatani. Statistical optimization for geometric computation:theory and practice. Elsevier. 1996
[115]A. W. Fitzgibbon, M. Pilu, R. B. Fisher. Direct least squares fitting of ellipses. Pattern Recognition,1996., Proceedings of the 13th International Conference on.1996.253-257 vol.1
[116]K. Kanatani. Statistical bias of conic fitting and renormalization. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1994.16(3).320-326
[117]K. Voss, H. Suesse. A new one-parametric fitting method for planar objects. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1999.21 (7).646-651
[118]B. Matei, P. Meer. A general method for Errors-in-Variables problems in computer vision. Computer Vision and Pattern Recognition,2000. Proceedings. IEEE Conference on.2000. 18-25 vol.2
[119]K. Kanatani. Geometric computation for machine vision Oxford University Press.1993
[120]Ellipse. http://mathworld.wolfram.com/Ellipse.html
[121]V. Fremont, R. Chellali. Direct camera calibration using two concentric circles from a single view, lnt Conf Artif Real Telexistence 2002.93-98
[122]J. Matas, O. Chum, M. Urban, T. Pajdla. Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing.2004.22(10).761-767
[123]R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision.2nd. Cambridge University Press.2003
[124]S. Bougnoux. From projective to Euclidean space under any practical situation, a criticism of self-calibration. The Sixth International Conference on Computer Vision.1998.790-796
[125]G. Zhang, X. Qin, W. Hua, T.-T. Wong, P.-A. Heng, H. Bao. Robust Metric Reconstruction from Challenging Video Sequences. IEEE Conference on Computer Vision and Pattern Recognition.2007.
[126]N. Snavely, S. M. Seitz, R. Szeliski. Photo Tourism:Exploring Photo Collections in 3D. ACM SIGGRAPH. Boston, Massachusetts.2006. ACM.
[127]P. Sturm, Z. L. Cheng, P. C. Y. Chen, A. N. Poo. Focal length calibration from two views: method and analysis of singular cases. Computer Vision and Image Understanding.2005. 99(1).58-95
[128]Y.-j. Zhou, X.-j. Kou. A practical iterative two-view metric reconstruction with uncalibrated cameras. Journal of Zhejiang University-Science A.2007.8(10).1614-1623
[129]K. Kanatani, C. Matsunaga. Closed-Form Expression for Focal Lengths from the Fundamental Matrix. Proc.4th Asian Conf. Computer Vision.2000.128-133
[130]T. Ueshiba, F. Tomita. Self-calibration from two perspective views under various conditions: Closed-form solutions and degenerate configurations. Proc. Australia-Japan Advanced Workshop on Computer Vision.2003.118-125
[131]K. Kanatani, A. Nakatsuji, Y. Sugaya. Stabilizing the Focal Length Computation for 3-D Reconstruction from Two Uncalibrated Views. International Journal of Computer Vision.2006. 66(2).109-122
[132]H. Stewenius. D. Nister, F. Kahl. F. Schaffalitzky. A Minimal Solution for Relative Pose with Unknown Focal Length. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2005. IEEE Computer Society.
[133]Z. Kukelova, M. Bujnak, T. Pajdla. Automatic Generator of Minimal Problem Solvers. Computer Vision - ECCV 2008.2008. Springer Berlin/Heidelberg.302-315
[134]M. Byrod. K. Josephson. K. Astrom. Improving Numerical Accuracy of Grobner Basis Polynomial Equation Solvers. Computer Vision,2007.ICCV 2007. IEEE 11th International Conference on.2007.1-8
[135]H. Li. A simple solution to the six-point two-view focallength problem. European Conference on Computer Vision.2006.200-213
[136]B. Bocquillon, A. Bartoli, P. Gurdjos, A. Crouzil. On Constant Focal Length Self-Calibration From Multiple Views. IEEE Conference on Computer Vision and Pattern Recognition.2007. 1-8
[137]B. Triggs. Autocalibration and the absolute quadric. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.1997.609-614
[138]D. Nister. Untwisting a Projective Reconstruction. Int. J. Comput. Vision.2004.60(2). 165-183
[139]M. Chandraker, S. Agarwal, F. Kahl, D. Nister, D. Kriegman. Autocalibration via Rank-Constrained Estimation of the Absolute Quadric. IEEE Conference on Computer Vision and Pattern Recognition.2007.1-8
[140]J. B. Lasserre. Global Optimization with Polynomials and the Problem of Moments. SIAM J. on Optimization.2001.11 (3).796-817
[141]F. Kahl, D. Henrion. Globally optimal estimates for geometric reconstruction problems. Tenth IEEE International Conference on Computer Vision.2005.978-985
[142]A. Habed, B. Boufama. Camera self-calibration from bivariate polynomials derived from Kruppa's equations. Pattern Recogn.2008.41(8).2484-2492
[143]雷成,胡占义,吴福朝,H.T.TSUI.一种新的基于Kruppa方程的摄像机自标定方法.计算机学报2003.26(5).
[144]D. Roble. Vision in film and special effects. SIGGRAPH Comput. Graph.1999.33(4).58-60
[145]K. Cornelis, M. Pollefeys, L. Van Gool. Tracking based structure and motion recovery for augmented video productions. ACM Symposium on Virtual Reality Software and Technology. 2001.17-24
[146]H. Anton van den, D. Anthony, T. Thorsten, hlen, W. Ben, H. S. T. Philip. VideoTrace:rapid interactive scene modelling from video. ACM SIGGRAPH 2007 papers. San Diego. California.2007. ACM.
[147]J. Oliensis. A Critique of Structure-from-Motion Algorithms. Computer Vision and Image Understanding.2000.80(2).172-214
[148]C. Zeller, O. Faugeras. Camera Self-Calibration from Video Sequences:the Kruppa Equations Revisited. RR-2793. INRIA.1996
[149]M. Pollefeys, L. van Gool. Stratified self-calibration with the modulus constraint. Pattern Analysis and Machine Intelligence, IEEE Transactions on.1999.21(8).707-724
[150]L. Maxime, Q. Long. Quasi-Dense Reconstruction from Image Sequence. Proceedings of the 7th European Conference on Computer Vision-Part Ⅱ.2002. Springer-Verlag.
[151]M. Pollefeys, F. Verbiest, L. Van Gool. Surviving Dominant Planes in Uncalibrated Structure and Motion Recovery. Computer Vision — ECCV 2002.2002.613-614
[152]J. Repko, M. Pollefeys.3D models from extended uncalibrated video sequences:addressing key-frame selection and projective drift. Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling.2005.150-157
[153]S. Jianbo, C. Tomasi. Good features to track. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.1994.593-600
[154]D. Chen, G. Zhang. A New Sub-Pixel Detector for X-Corners in Camera Calibration Targets. WSCG Short Papers.2005.97-100
[155]L. Matthews, T. Ishikawa, S. Baker. The template update problem. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2004.26(6).810-815
[156]S. Gibson, J. Cook, T. Howard, R. A. H. R. Hubbold, D. A. O. D. Oram. Accurate camera calibration for off-line, video-based augmented reality. Proceedings of the International Symposium on Mixed and Augmented Reality (ISMAR'02).2002.37-46
[157]P. H. S. Torr, A. Zisserman. MLESAC:a new robust estimator with application to estimating image geometry. Comput. Vis. Image Underst.2000.78(1).138-156
[158]F. Schaffalitzky. A. Zisserman, R. Hartley, P. Torr. A Six Point Solution for Structure and Motion. Computer Vision - ECCV 2000.2000.632-648
[159]D. Nister. Reconstruction from Uncalibrated Sequences with a Hierarchy of Trifocal Tensors. Computer Vision - ECCV 2000.2000.649-663
[160]M. Vergauwen, L. Van Gool. Web-based 3D Reconstruction Service. Machine Vision and Applications.2006.17(6).411-426
[161]M. Byrod, K. Josephson, K. Astrom. Fast Optimal Three View Triangulation. Computer Vision-ACCV 2007.2007.549-559
[162]R. I. Hartley, P. Sturm. Triangulation. Comput. Vis. Image Underst.1997.68(2).146-157
[163]H. S. Sawhney, Y. Guo, J. Asmuth, R. Kumar. Multi-view 3D estimation and applications to match move. Multi-View Modeling and Analysis of Visual Scenes,1999. (MVIEW '99) Proceedings. IEEE Workshop on.1999.21-28
[164]S. J. Sheather, M. C. Jones. A Reliable Data-Based Bandwidth Selection Method for Kernel Density Estimation. Journal of the Royal Statistical Society. Series B (Methodological).1991. 53(3).683-690
[165]J. Nocedal, S. Wright. Numerical Optimization. New York. Springer.2000
[166]B. K. P. Horn, H. M. Hilden. S. Negahdaripour. Closed-form solution of absolute orientation using orthonormal matrices. J. Opt. Soc.Am. A.1988.5(7).1127-1135
[167]C. Olsson, F. Kahl, M. Oskarsson. The Registration Problem Revisited:Optimal Solutions From Points, Lines and Planes. Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).2006.1206-1213
[168]J. Yekeun, K. In-So. Relative scale estimation between two camera motions. Pattern Recognition,2008. ICPR 2008.19th International Conference on.2008.1-4
[169]I. Esteban, L. Dorst, J. Dijk. Closed Form Solution for the Scale Ambiguity Problem in Monocular Visual Odometry. Intelligent Robotics and Applications. Springer Berlin/ Heidelberg.2010.665-679
[170]F. Fraundorfer, D. Scaramuzza, M. Pollefeys. A Constricted Bundle Adjustment Parameterization for Relative Scale Estimation in Visual Odometry. IEEE International Conference on Robotics and Automation.2010.
[171]R. Hartley, F. Schaffalitzky. L_infin minimization in geometric reconstruction problems. Proceedings of the Conference on Computer Vision and Pattern Recognition.2004. 1-504-1-509 Vol.1
[172]F. Kahl, R. Hartley. Multiple-View Geometry Under the L-infinity-Norm. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2008.30(9).1603-1617
[173]M. S. Lobo, L. Vandenberghe, S. Boyd, H. Lebret. Applications of second-order cone programming. Linear Algebra and its Applications.1998.284(1-3).193-228
[174]D. Martinec, T. Pajdla.3D Reconstruction by Gluing Pair-Wise Euclidean Reconstructions, or "How to Achieve a Good Reconstruction from Bad Images".3D Data Processing, Visualization, and Transmission, Third International Symposium on.2006.25-32
[175]J. F. Sturm. Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones. Optimization Methods and Software.1999.11(1).625-653
[176]I. E. Sutherland. Three-dimensional data input by tablet. Proceedings of the IEEE.1974.62(4). 453-461
[177]R. M. Haralick, L. G. Shapiro. Computer and Robot Vision. Prentice Hall 1993
[178]P. A. Beardsley, P. H. S. Torr, A. Zisserman.3D Model Acquisition from Extended Image Sequences. Proceedings of the 4th European Conference on Computer Vision-Volume Ⅱ-Volume Ⅱ.1996. Springer-Verlag.
[179]Community Photo Collections:http://grail.cs.washington.edu/projects/cpc/.
[180]F. Schaffalitzky, A. Zisserman. Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?". Computer Vision — ECCV 2002.2002.414-431
[181]J. Yao, W.-K. Cham. Robust multi-view feature matching from multiple unordered views. Pattern Recognition.2007.40(11).3081-3099
[182]X. Zeng, Q. Wang, J. Xu. MAP Model for Large-scale 3D Reconstruction and Coarse Matching for Unordered Wide-baseline Photos. BMVC.2008.
[183]R. Gherardi, M. Farenzena, A. Fusiello. Improving the efficiency of hierarchical structure-and-motion. Computer Vision and Pattern Recognition (CVPR),2010 IEEE Conference on.2010.1594-1600
[184]M. Brown, D. G. Lowe. Unsupervised 3D object recognition and reconstruction in unordered datasets.3-D Digital Imaging and Modeling,2005.3DIM 2005. Fifth International Conference on.2005.56-63
[185]N. Snavely, S. M. Seitz, R. Szeliski. Skeletal graphs for efficient structure from motion. Computer Vision and Pattern Recognition,2008. CVPR 2008. IEEE Conference on.2008.1-8
[186]C. Olsson, O. Enqvist. Stable Structure from Motion for Unordered Image Collections. Image Analysis. Springer Berlin/Heidelberg.2011.524-535
[187]T. Werner, T. Pajdla, M. Urban. Practice of 3D Reconstruction from Multiple Uncalibrated Unorganized Images. Proceedings of the Czech Pattern Recognition Workshop.2000.
[188]D. Martinec, T. Pajdla.3D reconstruction by fitting low-rank matrices with missing data. Computer Vision and Pattern Recognition,2005. CVPR 2005. IEEE Computer Society Conference on.2005.198-205 vol.1
[189]H. Cornelius, R. Sara, D. Martinec, T. Pajdla, O. Chum, J. Matas. Towards Complete Free-Form Reconstruction of Complex 3D Scenes from an Unordered Set of Uncalibrated Images. ECCV Workshop SMVP.2004.1-12
[190]A. Heyden. Projective Structure and Motion from Image Sequences using Subspace Methods. Proc.10th Scandinavian Conference on Image Analysis.1997.963-968
[191]M. Havlena, A. Torii, J. Knopp, T. Pajdla. Randomized Structure from Motion Based on Atomic 3D Models from Camera Triplets. Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2009.2874-2881
[192]M. Bujnak, Z. Kukelova, T. Pajdla.3D reconstruction from image collections with a single known focal length. Computer Vision,2009 IEEE 12th International Conference on.2009. 1803-1810
[193]D. Martinec, T. Pajdla. Robust Rotation and Translation Estimation in Multiview Reconstruction. Computer Vision and Pattern Recognition,2007. CVPR '07. IEEE Conference on.2007.1-8
[194]R. Datta, D. Joshi. J. Li, J. Z. Wang. Image retrieval:Ideas, influences, and trends of the new age. ACM Comput. Surv.2008.40(2).1-60
[195]C. Harris, M. Stephens. A Combined Corner and Edge Detector. Proceedings of the Fourth Alvev Vision Conference.1988.147-152
[196]K. Mikolajczyk, C. Schmid. Indexing based on scale invariant interest points. Computer Vision.2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on.2001. 525-531 vol.1
[197]P. R. Beaudet. Rotationally invariant image operators. Proceedings of the 4th International Joint Conference on Pattern Recognition. Tokyo.1978.579-583
[198]K. Mikolajczyk, C. Schmid. A Performance Evaluation of Local Descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on.2005.27(10).1615-1630
[199]S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, A. Y. Wu. An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. ACM.1998.45(6).891-923
[200]P. Torr, A. W. Fitzgibbon, A. Zisserman. Maintaining multiple motion model hypotheses over many views to recover matching and structure. Computer Vision,1998. Sixth International Conference on.1998.485-491
[201]P. H. S. Torr, A. W. Fitzgibbon, A. Zisserman. The Problem of Degeneracy in Structure and Motion Recovery from Uncalibrated Image Sequences. International Journal of Computer Vision.1999.32(1).27-44
[202]J. Joseph B. Kruskal. On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem. Proceedings of the American Mathematical Society.1956.48-50
[203]J.-K. Seo, H.-K. Hong, C.-W. Jho. M.-H. Choi. Two quantitative measures of inlier distributions for precise fundamental matrix estimation. Pattern Recognition Letters.2004. 25(6).733-741
[204]D. Oram. Projective Reconstruction and Metric Models from Uncalibrated Video SequencesPh.D. Degree. Manchester university.2001
[205]M. Urban, T. Pajdla, V. Hlavac. Projective Reconstruction from N Views Having One View in Common. Vision Algorithms:Theory and Practice.2000.116-131
[206]B. Triggs. Factorization methods for projective structure and motion. Computer Vision and Pattern Recognition,1996. Proceedings CVPR'96.1996 IEEE Computer Society Conference on.1996.845-851
[207]P. A. Parrilo. Semidefinite programming relaxations for semialgebraic problems. Mathematical Programming.2003.96(2).293-320
[208]D. Henrion, J.-B. Lasserre. GloptiPoly:Global optimization over polynomials with Matlab and SeDuMi. ACM Trans. Math. Softw.2003.29(2).165-194
[209]C. Strecha, W. v. Hansen, L. V. Gool, P. Fua, U. Thoennessen. On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery. CVPR.2008.
[210]W. Niem. Automatic reconstruction of 3D objects using a mobile camera. Image and Vision Computing.1999.17(2).125-134
[211]F. Schmitt, Y. Yemez.3D color object reconstruction from 2D image sequences. Image Processing,1999. ICIP 99. Proceedings.1999 International Conference on.1999.65-69 vol.3
[212]B. Adam, L. Alex, T. Richard.3D S.O.M.:a commercial software solution to 3D scanning. Graph. Models.2005.67(6).476-495.
[213]M. Pollefeys, L. Van Gool, M. Vergauwen, F. Verbiest, K. Cornelis, J. Tops, R. Koch. Visual Modeling with a Hand-Held Camera. International Journal of Computer Vision.2004.59(3). 207-232
[214]R. Koch, J.-F. Evers-Senne, J.-M. Frahm, K. Koeser.3D Reconstruction and Rendering from Image Sequences WIAMIS.2005.
[215]E. Imre, S. Knorr, B. Ozkalayci, U. Topay, A. Aydin Alatan, T. Sikora. Towards 3-D scene reconstruction from broadcast video. Signal Processing:Image Communication.2007.22(2). 108-126
[216]C.-M. Cheng, S.-F. Wang, C.-H. Teng, S.-H. Lai. Image-based three-dimensional model reconstruction for Chinese treasure--Jadeite Cabbage with Insects. Computers & Graphics. 2008.32(6).682-694
[217]Y. Y. Boykov, M. P. Jolly. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. Computer Vision,2001.1CCV 2001. Proceedings. Eighth IEEE International Conference on.2001.105-112 vol.1
[218]陈京,袁保宗.丛于视觉凸包的真实感模型重建[技术报告].北京交通大学信息科学研究所.2009
[219]W. E. Lorensen, H. E. Cline. Marching cubes:A high resolution 3D surface construction algorithm. SIGGRAPH Comput. Graph.1987.21(4).163-169
[220]陈京,袁保宗.丛于视频的模型重建中帧优化选择方法[技术报告].北京交通大学信息科学研究所.2010
[221]F. Bernardini, J. Mittleman. H. Rushmeier, C. Silva, G. Taubin. The Ball-Pivoting Algorithm for Surface Reconstruction. IEEE Transactions on Visualization and Computer Graphics.1999. 5(4).349-359
[222]M. Garland, P. S. Heckbert. Surface simplification using quadric error metrics. Proceedings of the 24th annual conference on Computer graphics and interactive techniques.1997. ACM Press/Addison-Wesley Publishing Co.
[223]M. Attene, B. Falcidieno, M. Spagnuolo. Hierarchical mesh segmentation based on fitting primitives. The Visual Computer.2006.22(3).181-193
[224]A. Watt, F. Policarpo.3D Games:Animation and Advanced Real-time Rendering, Volume2(3D游戏第二卷动画与高级实时渲染技术).Addison Wesley.2003
[225]S. R. Buss.3D Computer Graphics:A Mathematical Introduction with OpenGL. Cambridge University Press 2003
[226]F. Pighin, J. Hecker, D. Lischinski, R. Szeliski, D. H. Salesin. Synthesizing realistic facial expressions from photographs. Proceedings of the 25th annual conference on Computer graphics and interactive techniques.1998. ACM.
[227]陈京,袁保宗.丛于匹配点的真实感模型重建[技术报告].北京交通大学信息科学研究所.2010
[228]S. M. Seitz, B. Curless, J. Diebel, D. Scharstein, R. Szeliski. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms. Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on.2006.519-528
[229]http://cmp.felk.cvut.cz/projects/is3d/Data.html.
[230]Y. Ma, K. Huang, R. Vidal, Koeck, aacute, J, S. Sastry. Rank Conditions on the Multiple-View Matrix. International Journal of Computer Vision.2004.59(115-137)
[231]C. P. Lu, G. D. Hager. E. Mjolsness. Fast and globally convergent pose estimation from video images. Pattern Analysis and Machine Intelligence. IEEE Transactions on.2000.22(6). 610-622
[232]L. Hinsken. A Singularity Free Algorithm for Spatial Orientation of Bundles. International Archives of Photogrammetry andRemote Sensing. Kyoto, Japan.1988.
[233]E. M. Mikhail, J. S. Bethel, J. C. McGlone. Introduction to Modern Photogrammetry Wiley. 2001
[234]R. I. Hartley. Cheirality invariants. DARPA Image Understanding Workshop.1993.745-753
© 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 |