计算机视觉三维重建理论与应用
|
摘要
计算机立体视觉研究利用二维投影图像恢复三维景物世界的问题，是计算机视觉技术的主要任务之一。计算机三维重建理论及其实现技术在战场三维地形建模、战场监视以及精确导航等领域有着重要的应用。根据军队建设对快速三维地形重建和快速精确定位的需要，本文对立体视觉计算中的影像特征提取、基于知识的影像分析、宽基线影像匹配、影像地面控制点自动识别、三维实时重建以及立体视觉模型误差分析等问题展开了理论和实现技术的研究，取得了一些研究成果。
图像基本特征获取是计算机三维重建多种算法的基础。通过对直线特征的分析，本文提出了一个新的层次结构化直线提取算法。该算法在不同尺度上利用局部区域直线特征构造直线段，形成一种具有层次的结构化的直线表示。算法采用自底向上的策略，首先构造底层局部线段结构，然后利用业已获得的下层结构构造上一层结构，因此具有较高的效率。算法既利用了局部特征，又利用了全局特征，因此具有较高的可靠性。
在区域特征提取方面，本文的贡献是提出了一个稳健的区域分割算法。该算法首先将影像划分为较为平滑的区域和亮度变化较为剧烈的区域。根据像点是否处于平滑区域采用不同的方法分别处理。对于处于平滑区域的像点采用简单算法处理，对于可能处于区域边缘的像点采用较为复杂的移动平均算法在色度空间和尺度空间上处理。因此，算法兼顾了时间效率和稳健性。
影像匹配是三维重建问题的核心。本文提出了一个面向三维重建任务的基于知识的图像分析系统模型。该模型将相关知识划分为基本影像处理知识、与具体目标影像相关的知识和与分析任务相关的知识。基于知识的图像分析系统运用基本影像分析知识和领域专家知识对场景中基本特征关系进行分析，获得场景中透视关系或其它特定空间几何关系的定性表达。目的是通过正确解释影像中某些空间关系来提高匹配算法的效率和可靠性。
在影像匹配算法方面，本文的主要贡献是提出了一个基于局部仿射不变量特征的宽基线条件下的影像匹配算法，处理最一般情况下的影像立体匹配。算法以影像特征点为定位点，使用分层变尺度窗口内的几何仿射不变量特征和亮度仿射不变量特征实现宽基线影像立体匹配。该算法利用局部特征进行宽基线立体匹配，因此具有较高的
算法效率。同时，算法在较大窗口范围内构造尺度、旋转不变特征，并考虑到可能存
在的投影变形而对窗口形状进行变换，因而具有较高的匹配可靠性。通过将大窗口划
分为较小的重叠子区域，算法可以获得较好的匹配精度并减少因遮挡造成的不利影响。
在三维重建方面，本文研究了三维射影重建和欧氏重建的基本算法、光束法平差
三维重建算法和实时Kalman滤波修正三维欧氏重建算法。探讨了基于平面图的单视
图约束和多视图约束三维重建算法。
在立体视觉模型方面，本文推导了标准立体视觉模型和汇聚立体视觉模型的三维
重建误差公式，分析了欧氏重建中不同立体模型的点位误差大小及其变化规律。
在计算机三维视觉理论与技术的应用方面，本文提出了一个快速数字地形重建系
统的设计与实现方法。该系统将交互式系统和实时系统集成在一起，利用交互式三维
重建系统获取高精度、高可靠性的三维模型，利用实时三维重建系统获取精度较低的
快速重建结果，通过数据融合消除不可靠的重建结果、提高重建精度。该系统由控制
测量子系统、交互式三维地形重建子系统、三维地形可视化子系统、基于GPS八NS的
实时三维地形重建子系统和基于平面图的三维重建子系统等多个部分构成，实现高精
度快速三维地形重建任务。该应用系统的设计和部分实现，进一步证明了本文理论研
究和应用研究成果的正确性和实用价值。
Computer stereo vision mainly studies the issues of reconstruction of the 3D world from the 2D images, and is one of the main tasks of computer vision. Computer stereo vision theories and techniques can be used in many important areas such as 3D battlefields modeling, battlefields situation monitoring, positioning and navigation. To meet the requirement of the military development for the 3D terrain reconstruction and positioning rapidly and precisely, this dissertation mainly studies the issues of image features detection and knowledge based image features spatial relation analysis, wide baseline stereo matching, real-time 3D reconstruction and error analysis about the stereo vision models, and proposes some novel and effective methods to cope with those problems.
Finding out basic features in an image is always the necessary step in many algorithms in computer stereo vision. After studying the property of the straight line, a novel hierarchical algorithm about straight lines extraction is proposed in the dissertation. This straight lines extraction method extracts local line segments from local image intensity features at different spatial scale. First, the algorithm forms many short lines in the lowest level. Then it merges them into upper level according to their adjacency and straight line constraints. Finally a hierarchical straight lines structure is formed and final straight lines were extracted.
In the area of region features detection, another contribution in this work is that it proposes a robust segmentation algorithm. This algorithm uses different methods dealing with different pixels in the image according to its position, namely using simple average method dealing with the pixels which are in the position of distance to edges of regions, and using the mean shift algorithm dealing with the other pixels, which are possibly near the edges of regions, both in color space and scale space. It is obvious that the proposed algorithm is effective and robust.
The key problem to 3D reconstruction is image correspondence. A knowledge based image analysis system framework is proposed in the work aiming to deal with the image
matching problems in 3D reconstruction. In this framework, knowledge can be classified as knowledge about image processing itself, knowledge about specific target images and knowledge about image processing tasks. Knowledge based image analysis system employs fundamental knowledge about image analysis and experts' knowledge in specialist area to find out spatial perspective relations among interested features in the scene or other relations such as perspective vanished point position and 3D parallel lines.
In this work, a novel algorithm based on local affine invariants is proposed for searching correspondences between images in the most general case, namely, under the wide baseline conditions and without any priori information about the internal or external camera parameters. This algorithm uses image point features as anchor points for image matching, and the local space-variants regions around the anchor point are used as local window region to generate geometric affine invariants and color affine invariants for image matching. The proposed stereo matching method is efficient in algorithmic time because only the local image features are used in the image matching process. Meanwhile, the image matching results are reliable and robust because its feature matching region is relatively large, and the shape of local window region is transformed according to possible projective deformation. In addition, overlapped sub-regions are used in local feature extraction to reduce partially occluding problems.
On 3D reconstruction, this dissertation discusses the basic methods of 3D projective reconstruction and 3D Euclidian reconstruction, a bundle adjustment algorithm for 3D reconstruction, a real-time Kalman filtering 3D Euclidian reconstruction method, and plane based single view and multi-view constraints algorithm for 3D Euclidian reconstruction.
In this work, stereo camera model error is al
图像基本特征获取是计算机三维重建多种算法的基础。通过对直线特征的分析，本文提出了一个新的层次结构化直线提取算法。该算法在不同尺度上利用局部区域直线特征构造直线段，形成一种具有层次的结构化的直线表示。算法采用自底向上的策略，首先构造底层局部线段结构，然后利用业已获得的下层结构构造上一层结构，因此具有较高的效率。算法既利用了局部特征，又利用了全局特征，因此具有较高的可靠性。
在区域特征提取方面，本文的贡献是提出了一个稳健的区域分割算法。该算法首先将影像划分为较为平滑的区域和亮度变化较为剧烈的区域。根据像点是否处于平滑区域采用不同的方法分别处理。对于处于平滑区域的像点采用简单算法处理，对于可能处于区域边缘的像点采用较为复杂的移动平均算法在色度空间和尺度空间上处理。因此，算法兼顾了时间效率和稳健性。
影像匹配是三维重建问题的核心。本文提出了一个面向三维重建任务的基于知识的图像分析系统模型。该模型将相关知识划分为基本影像处理知识、与具体目标影像相关的知识和与分析任务相关的知识。基于知识的图像分析系统运用基本影像分析知识和领域专家知识对场景中基本特征关系进行分析，获得场景中透视关系或其它特定空间几何关系的定性表达。目的是通过正确解释影像中某些空间关系来提高匹配算法的效率和可靠性。
在影像匹配算法方面，本文的主要贡献是提出了一个基于局部仿射不变量特征的宽基线条件下的影像匹配算法，处理最一般情况下的影像立体匹配。算法以影像特征点为定位点，使用分层变尺度窗口内的几何仿射不变量特征和亮度仿射不变量特征实现宽基线影像立体匹配。该算法利用局部特征进行宽基线立体匹配，因此具有较高的
算法效率。同时，算法在较大窗口范围内构造尺度、旋转不变特征，并考虑到可能存
在的投影变形而对窗口形状进行变换，因而具有较高的匹配可靠性。通过将大窗口划
分为较小的重叠子区域，算法可以获得较好的匹配精度并减少因遮挡造成的不利影响。
在三维重建方面，本文研究了三维射影重建和欧氏重建的基本算法、光束法平差
三维重建算法和实时Kalman滤波修正三维欧氏重建算法。探讨了基于平面图的单视
图约束和多视图约束三维重建算法。
在立体视觉模型方面，本文推导了标准立体视觉模型和汇聚立体视觉模型的三维
重建误差公式，分析了欧氏重建中不同立体模型的点位误差大小及其变化规律。
在计算机三维视觉理论与技术的应用方面，本文提出了一个快速数字地形重建系
统的设计与实现方法。该系统将交互式系统和实时系统集成在一起，利用交互式三维
重建系统获取高精度、高可靠性的三维模型，利用实时三维重建系统获取精度较低的
快速重建结果，通过数据融合消除不可靠的重建结果、提高重建精度。该系统由控制
测量子系统、交互式三维地形重建子系统、三维地形可视化子系统、基于GPS八NS的
实时三维地形重建子系统和基于平面图的三维重建子系统等多个部分构成，实现高精
度快速三维地形重建任务。该应用系统的设计和部分实现，进一步证明了本文理论研
究和应用研究成果的正确性和实用价值。
Computer stereo vision mainly studies the issues of reconstruction of the 3D world from the 2D images, and is one of the main tasks of computer vision. Computer stereo vision theories and techniques can be used in many important areas such as 3D battlefields modeling, battlefields situation monitoring, positioning and navigation. To meet the requirement of the military development for the 3D terrain reconstruction and positioning rapidly and precisely, this dissertation mainly studies the issues of image features detection and knowledge based image features spatial relation analysis, wide baseline stereo matching, real-time 3D reconstruction and error analysis about the stereo vision models, and proposes some novel and effective methods to cope with those problems.
Finding out basic features in an image is always the necessary step in many algorithms in computer stereo vision. After studying the property of the straight line, a novel hierarchical algorithm about straight lines extraction is proposed in the dissertation. This straight lines extraction method extracts local line segments from local image intensity features at different spatial scale. First, the algorithm forms many short lines in the lowest level. Then it merges them into upper level according to their adjacency and straight line constraints. Finally a hierarchical straight lines structure is formed and final straight lines were extracted.
In the area of region features detection, another contribution in this work is that it proposes a robust segmentation algorithm. This algorithm uses different methods dealing with different pixels in the image according to its position, namely using simple average method dealing with the pixels which are in the position of distance to edges of regions, and using the mean shift algorithm dealing with the other pixels, which are possibly near the edges of regions, both in color space and scale space. It is obvious that the proposed algorithm is effective and robust.
The key problem to 3D reconstruction is image correspondence. A knowledge based image analysis system framework is proposed in the work aiming to deal with the image
matching problems in 3D reconstruction. In this framework, knowledge can be classified as knowledge about image processing itself, knowledge about specific target images and knowledge about image processing tasks. Knowledge based image analysis system employs fundamental knowledge about image analysis and experts' knowledge in specialist area to find out spatial perspective relations among interested features in the scene or other relations such as perspective vanished point position and 3D parallel lines.
In this work, a novel algorithm based on local affine invariants is proposed for searching correspondences between images in the most general case, namely, under the wide baseline conditions and without any priori information about the internal or external camera parameters. This algorithm uses image point features as anchor points for image matching, and the local space-variants regions around the anchor point are used as local window region to generate geometric affine invariants and color affine invariants for image matching. The proposed stereo matching method is efficient in algorithmic time because only the local image features are used in the image matching process. Meanwhile, the image matching results are reliable and robust because its feature matching region is relatively large, and the shape of local window region is transformed according to possible projective deformation. In addition, overlapped sub-regions are used in local feature extraction to reduce partially occluding problems.
On 3D reconstruction, this dissertation discusses the basic methods of 3D projective reconstruction and 3D Euclidian reconstruction, a bundle adjustment algorithm for 3D reconstruction, a real-time Kalman filtering 3D Euclidian reconstruction method, and plane based single view and multi-view constraints algorithm for 3D Euclidian reconstruction.
In this work, stereo camera model error is al
引文
[1] A. Fusiello, V. Roberto, E. Trucco, Efficient Stereo with Multiple Windowing, Proc. Computer Vision and Pattern Recognition, pp.858-863, 1997.
[2] A. R. Mansouri, A. Mitiche, J. Konrad, Selective Image Diffusion: Application to Disparity Estimation, Proc. Int'l Conf. Image Processing, Vol.3, pp.284-288, 1998.
[3] Aaron F. Bobick, Stephen S. Intille. Large occlusion stereo. International Journal on Computer Vision, Vol.33, No.3, pp. 181-200, 1999.
[4] A. Goshtasby, G.C. Stockman, C.V. Page, A region-based approach to digital image registration with subpixel accuracy, IEEE Transactions on Geoscience and Remote Sensing, 1986(24), pp.390-399, 1986.
[5] B. D. Lucas, T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision, Proc. Int'l Joint Conf. A. I., pp.674-679, 1981.
[6] A.S. Vasileisky, B. Zhukov, M. Berger, Automated image coregistration based on linear feature recognition, Proceedings of the Second Conference Fusion of Earth Data, Sophia Antipolis, France, pp.59-66, 1998.
[7] B. Likar, F. Pernus, Automatic extraction of corresponding points for the registration of medical images, Medical Physics, 1999(26), pp. 1678-1686, 1999.
[8] C. Chang, S. Chatterjee, P. R. Kube, On an Analysis of Static Occlusion in Stereo Vision, Proc. Computer Vision and Pattem Recognition, pp. 722-723, 1991.
[9] C. Lawrence Zitnick, Takeo Kanade. A cooperative algorithm for stereo matching and occlusion detection. IEEE Transactions on Pattem Analysis and Machine Intelligence, Vol.22, No.7, pp.675-684, 2000.
[10] C. Schmid, A. Zisserman, A. Fitzgibbon, Automatic Line Matching and 3D Reconstruction of Buildings from Multiple Views, ISPRS Conference on Automatic Extraction of GIS Objects from Digital Imagery, IAPRS Vol.32, Part 3-2W5, pp.69-80, September 1999.
[11] C. Schmid, A. Zisserman, The Geometry and Matching of Curves in Multiple Views,
Proc. European Conf. Computer Vision, pp. 104-118, 1998.
[12] C. Silva, J. Santos-Victor, Intrinsic Images for Dense Stereo Matching with Occlusions, Proc. European Conf. Computer Vision, pp.100-114, 2000.
[13] C. Sun, A Fast Stereo Matching Method, Proc. Digital Image Computing: Techniques and Applications, pp.95-100, 1997.
[14] C. Tomasi, R. Manduchi, Stereo without Search, Technical Report STAN-CS-TR-95-1543, Stanford University, 1995.
[15] C. Tomasi, R. Manduchi, Stereo Matching as a Nearest-Neighbor Problem, IEEE Trans. Pattem Analysis and Machine Intelligence, Vol.20, pp.333-340, 1998.
[16] Carsten Rother and Stefan Carlsson, Linear Multi View Reconstruction and Camera Recovery, Proc. 8th Intemational Conference on Computer Vision, Vancouver, Canada, pp.42-49. 2001.
[17] D. Comaniciu, P. Meer, Mean Shift: A Robust Approach towards Feature Space Analysis, IEEE Transactions on Patter Analysis and Machine Intelligence, Vol.24, No.5, pp.603-619, May2002
[18] D. N. Bhat, S. K. Nayar, Ordinal Measures for Image Correspondence, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.20, pp.415-423, 1998.
[19] D. P. Robertson, R. Cipolla, Building Architectural Models from Many Views Using Map Constraints, Computer Vision-ECCV 2002, 7th European Conference on Computer Vision, Copenhagen, Denmark, Proceedings, Part Ⅱ. May 28-31, 2002.
[20] D. Scharstein, R. Szeliski, Stereo Matching with Non-Linear Diffusion, Int'l J. Computer Vision, Vol.28, No.2, pp. 155-174, 1998.
[21] D. Scharstein, R. Szeliski, A Taxonomy and Evaluation of Dense two-Frame Stereo Correspondence Algorithms, Int'l J. Computer Vision, Vol.47, No. 1, pp.7-42, 2002.
[22] D. H. Ballard, Generalizing the Hough Transform to Detect Arbitrary Shapes, Pattern Recognition, Vol.13, No.2, pp.111-122, 1981.
[23] B.S. Manjunath, C. Shekhar, R. Chellapa, A new approach to image feature detection with applications, Pattern Recognition, Vol.29, pp.627-640, 1996.
[24] F. Bigone, O. Henricsson, P. Fua, M. Stricker, Automatic Extraction of Generic House Roofs from High Resolution Aerial Imagery, Proc. European Conf. Computer Vision,
pp.85-96, 1996.
[25] H. Li, M. A. Lavin, R. J. LeMaster, Fast Hough Transform: A Hierarchical Approach, Computer Vision, Graphics, and Image Processing, Vol.36, No.2, pp. 139-161, 1986.
[26] H. P. Moravec, Towards automatic visual obstacle avoidance. Proceedings of the 5th International Joint Conference on Artificial Intelligence, Carnegie-Mellon University, Pittsburgh, PA, August 1977.
[27] H. Yamamoto, Y. Yeshurun, M. D. Levine. An active foveated vision system: Attentional mechanisms and scan path covergence measures. Journal of Computer Vision and Image Understanding, 63(1), pp.50-65, 1996.
[28] H. Zhao, Global Optimal Surface from Stereo, Proc. Int'l Conf. Pattern Recognition, Vol. 1, pp. 101-104, 2000.
[29] Hartley R, Self-calibration of stationary cameras, International Journal of Computer Vision, Vol.22, No. 1, pp.5-23, 1997.
[30] Heiko Hirschmuller. Improvements in real-time correlation-based stereo vision. In Proceedings of Workshop on Stereo and Multi-Baseline Vision, Kauai, Hawaii, pp.141-148, 2001.
[31] Heung-Yeung Shum, Mei Han, Richard Szeliski, Interactive Construction of 3D Models from Panoramic Mosaics, Proc. of CVPR'98, June, 1998.
[32] B. Zitova, J. Kautsky, G. Peters, J. Flusser, Robust detection of significant points in multiframe images, Pattern Recognition Letters 1999(20), pp. 199-206, 1999.
[33] I. J. Cox, S. L. Hingorani, S. B. Rao, B. M. Maggs, A Maximum Likelihood Stereo Algorithm, Computer Vision and Image Understanding, Vol.63, pp.542-567, 1996.
[34] I. Thomos, S. Malasiotis, M. G. Strintzis, Optimized Block Based Disparity Estimation in Stereo Systems Using a Maximum-Flow Approach, Proc. SIBGRAPI'98 Conf., 1998.
[35] J. B. Bums, A. Hanson, E. Riseman, Extracting straight lines., IEEE Transaction on Pattern Analysis and Machine Intelligent, Vol.8, No.4, pp.425-455.1986.
[36] J Cooper, S Venkatesh, L Kitchen. Early jump-out comer detections. IEEE Transactions on Pattern Analysis and Machine Intelligence. 15, pp.823-828, 1993
[37] J. J. Little, W. E. Gillett, Direct Evidence for Occlusion in Stereo and Motion, Image
and Vision Computing, Vol.8, No.4, pp.328-340, 1990.
[38] J. Shah, A Nonlinear Diffusion Model for Discontinuous Disparity and Half Occlusions in Stereo, Proc. Computer Vision and Pattern Recognition, pp.34-40, 1993.
[39] Jian Sun, Heung-Yeung Shum, Nan-Ning Zheng. Stereo matching using belief propagation. In Proceedings 7th European Conference on Computer Vision, Vol.2, pp.510-524, Copenhagen, Denmark, May 2002.
[40] J. W. Gates, M. Haseyama, H. Kitajima, A Real-time Line Extraction Method, IEEE International Symposium on Circuits and Systems'99, Vol. Ⅳ, pp.68-71, 1999.
[41] J. Woodfill, B. Von Herzen, Real-Time Stereo Vision on the PARTS Reconfigurable Computer, Proc. IEEE Workshop on FPGAs for Custom Computing Machines, pp.242-250, 1997.
[42] Masatoshi Okutomi, Yasuhiro Katayama, Setsuko Oka. A simple stereo algorithm to recover precise object boundaries and smooth surfaces. In Proceedings of Workshop on Stereo and Multi-Baseline Vision, pp. 158-165, 2001.
[43] Yonatan Wexler, Andrew W. Fitzgibbon, Andrew Zisserman, Learning epipolar geometry from image sequences, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2003.
[44] Joseph Giarratano, Gary Riley, Expert system principles and programming, PWS Publishing Company, 1998.
[45] K. N. Kutulakos, S. M. Seitz, A Theory of Shape by Space Carving, Int'l J. Computer Vision, Vol.38, No.3, pp.199-218, 2000.
[46] K. Muhlmann, D. Maier, J. Hesser, R. Manner, Calculating Dense Disparity Maps from Color Stereo Images, an Efficient Implementation, Proc. IEEE Workshop on Stereo and Multi-Baseline Vision, pp.30-36, 2001.
[47] K. Satoh, Y. Ohta, Occlusion Detectable Stereo-Systematic Comparison of Detection Algorithms, Proc. Int'l Conf. Pattern Recognition, pp.280-286, 1996.
[48] L. Matthies, A. Kelly, T. Litwin, G. Tharp, Obstacle Detection for Unmanned Ground Vehicles: A Progress Report, Proc. Intelligent Vehicles'95 Symp., pp.66-71, 1995.
[49] L. Shafarenko, M. Petrou, Automatic watershed segmentation of randomly textured color images. IEEE Transactions on Image Processing, Vol.6, No.11, pp.1530-44, 1997.
[50] L. Xu, E. Oja, Randomized Hough Transform (RHT)-Basic Mechanisms, Algorithms, and Computational Complexities, CVGIP-Image Understanding, Vol.57, No.2, pp.131-154, 1993.
[51] M. Boldt, R. Weiss, E. Riseman, Token-based extraction of straight lines, IEEE Transaction on System, Man Cybernetics, Vol. 19, No.7, pp. 1581-1594. 1989.
[52] M. K. Reed, P. K. Allen, Constraint-Based Sensor Planning for Scene Modeling, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.22, pp. 1460-1467, 2000.
[53] M. Pilon, P. Cohen, Occlusion Detection and Interpretation Based on Image Reprojection, Proc. S. W. Symp. Image Analysis and Interpretation, pp. 166-171, 1996.
[54] M. Tistarelli, G. Sandini. On the advantages of polar and log-polar mapping for direct estimation of time-to-impact from optical flow, IEEE Transaction on PAMI, Vol.15, No.4, pp.401-410, 1993.
[55] M. Z. Brown, D. Burschka, G. D. Hager, Advances in Computational Stereo, Vol.25, No.8, pp.993-1008, 2003.
[56] D. Bhattacharya, S. Sinha, Invariance of stereo images via theory of complex moments, Pattern Recognition (30) pp. 1373-1386. 1997.
[57] Marc Bolduc, Martin D. Levine, A Review of Biologically-Motivated Space-Variant Data Reduction Models, Computer Vision and Image Understanding, Vol. 69, No. 2, pp.170-184, 1998.
[58] Matthew E. Antone, Seth Teller, Automatic Recovery of Relative Camera Rotations for Urban Scenes, Computer Vision and Pattern Recognition, CVPR'OO, Vol.2, pp.2282, June, 2000.
[59] H.S. Alhichri, M. Kamel, Virtual circles: a new set of features for fast image registration, Pattem Recognition Letters (24), pp. 1181-1190. 2003.
[60] J. Matas, S. Obdrzalek, O. Chum, Local affine frames for widebaseline stereo, R. Kasturi, D. Laurendeau, C. Suen (Eds.), 16th International Conference on Pattern Recognition ICPR 2002, Vol.4, pp.363-366. 2002.
[61] Minglun Gong, Yee-Hong Yang. Multi-resolution stereo matching using genetic algorithm, In Proceedings of Workshop on Stereo and Multi-Baseline Vision, pp.21-29, Kauai, Hawaii, December 2001.
[62] O. Chutatape, L. Guo, A Modified Hough Transform for Line Detection and its Performance, Pattern Recognition, Vol.32, No.2, pp. 181-182, Feb. 1999.
[63] K. Rohr, Landmark-Based Image Analysis: Using Geometric and Intensity Models, Computational Imaging and Vision Series, Vol.21, Kluwer Academic Publishers, Dordrecht, 2001.
[64] O. Faugeras, et al, Real Time Correlation-Based Stereo: Algorithm, Implementations and Applications, INRIA Technical Report 2013, 1993.
[65] O. Faugeras, Q.-T. Luong, The Geometry of Multiple Images, The MIT Press, Cambridge, Massachusetts, 2001.
[66] O. Zuniga, R. M. Haralick. Comer detection using the facet model, Computer Vision and Pattern Recognition, pp.30-37, IEEE, Los Alamitos, CA, 1983.
[67] P. Aschwanden, W. Guggenbuhl, Experimental Results from a Comparative Study on Correlation Type Registration Algorithms, In Robust Computer Vision, Wickmann, pp.268-289, 1993.
[68] Paul Beardsley, Phil Torr, Andrew Zisserman, 3D Model Acquisition from Extended Image Sequences, Proc. 4th European Conference on Computer Vision, Cambridge; LNCS 1065, Vol. Ⅱ, pp.683-685, Springer-Verlag, 1996.
[69] P. Corke, P. Dunn, Real-Time Stereopsis Using FPGAs, Proc. IEEE TENCON-Speech and Image Technologies for Computing and Telecom, pp.235-238, 1997.
[70] P. Fua, A Parallel Stereo Algorithm that Produces Dense Depth Maps and Preserves Image Features, Machine Vision and Applications, Vol.6, pp.35-49, 1993.
[71] P. Fua, Y. G. Leclerc, Object-Centered Surface Reconstruction: Combining Multi-image Stereo and Shading, Int'l J. Computer Vision, Vol. 16, pp.35-56, 1995.
[72] P. N. Belhumeur, A Bayesian Approach to Binocular Stereopsis, Int'l J. Computer Vision, Vol.19, No.3, pp.237-260, 1996.
[73] Pollefeys M, Van Gool L, A stratified approach to metric self-calibration, Proc IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, California, pp.407-412, 1997.
[74] Pollefeys M, Koch R, Van Gool L., Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters. In: Proc International Conference on Computer Vision, Bombay, India, pp.90-95, 1998.
[75] R. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, Cambridge, United Kingdom, 2000.
[76] R. Nelson, Finding line segments by stick growing, IEEE Transaction on Pattern Analysis and Machine Intelligent, Vol. 16, No.5, pp.519-523. 1994.
[77] R. Wallace, P. Wen Ong, B. Bederson, E. Schwartz. Space variant image processing. International Journal of Computer Vision, Vol. 13, No. 1, pp.71-90, 1994.
[78] R. Wildes, Direct Recovery of Three-Dimensional Scene Geometry from Binocular Stereo Disparity, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 13, No.8, pp.761-774, 1991.
[79] R. Zabih, J. Woodfill, Non-Parametric Local Transforms for Computing Visual Correspondence, Proc. 3rd European Conf. Computer Vision, pp. 150-158, 1994.
[80] R. Sara, R. Bajcsy, On Occluding Contour Artifacts in Stereo Vision, Proc. Computer Vision and Pattern Recognition, pp.852-857, 1997.
[81] D. Tell, S. Carlsson, Wide baseline point matching using affine invariants computed from intensity profiles, European Conference on Computer Vision, Vol. 1, pp.814-828, 2000.
[82] Radim Sara, Finding the largest unambiguous component of stereo matching, In Proceedings 7th European Conference on Computer Vision, Vol.3, pp.900-914, 2002.
[83] Richard Szeliski, Daniel Scharstein, Symmetric sub-pixel stereo matching, In Proceedings 7th European Conference on Computer Vision, Vol.2, pp.525-540, 2002.
[84] P. Pritchett, A. Zisserman, Wide Baseline Stereo, Int'l Conference on Computer Vision, pp.754-759, 1998.
[85] S. Belongie, C. Carson, Color-and texture-based image segmentation using EM and its application to content-based image retrieve, Proc. of ICCV, pp.675-82, 1998.
[86] S. Birchfield, C. Tomasi, Depth Discontinuities by Pixel-to-Pixel Stereo, Proc. IEEE Int'l Conf. Computer Vision, pp.1073-1080, 1998
[87] S. Birchfield, C. Tomasi, Multiway Cut for Stereo and Motion with Slanted Surfaces, Proc. Int'l Conf. Computer Vision, Vol. 1, pp.489-495, 1999
[88] S. D. Cochran, G. Medioni, 3-D Surface Description from Binocular Stereo, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 14, No. 10, pp.981-994, 1992.
[89] S. Kimura, T. Shinbo, H. Yamaguchi, E. Kawamura, K. Naka, A Convolve-Based Real-Time Stereo Machine (SAZAN), Proc. Computer Vision and Pattern Recognition, Vol. 1, pp.457-463, 1999.
[90] S. M. Smith, J. M. Brady, SUSAN-A New Approach to Low Level Image Processing. Int. Journal of Computer Vision, Vol.23, No.1, pp.45-78, May, 1997.
[91] S. Randriamasy, A. Gagalowicz, Region Based Stereo Matching Oriented Image Processing, Proc. Computer Vision and Pattern Recognition, pp.736-737, 1991.
[92] S. Roy, I. J. Cox, A Maximum-Flow Formulation of the N-camera Stereo Correspondence Problem, Proc. Int'l Conf. Computer Vision, pp.492-499, 1998.
[93] S.S. Intille, A. F. Bobick, Incorporating Intensity Edges in the Recovery of Occlusion Regions, Proc. Int'l Cone Pattern Recognition, Vol. 1, pp.674-677, 1994.
[94] S. Scherer, P. Werth, A. Pinz, The Discriminatory Power of Ordinal Measures-Towards a New Coefficient, Proc. Computer Vision and Pattern Recognition, Vol. 1, pp.76-81, 1999.
[95] S. T. Bamard, M. A. Fischler, Computational Stereo, ACM Computing Surveys, Vol.14, pp.553-572, 1982.
[96] Ye Zhang, Chandra Kambhamettu. Stereo matching with segmentation-based cooperation. In Proceedings 7th European Conference on Computer Vision, Vol. 2, pp.556-571, Copenhagen, Denmark, May 2002.
[97] Sebastien Roy. Stereo without epipolar lines: A maximum-flow formulation. International Journal of Computer Vision, Vol.34, No.3, pp.147-161, 1999.
[98] T. Kanade, M. Okutomi, T. Nakahara, A Multiple-baseline Stereo Method, Proc. ARPA Image Understanding Workshop, pp.409-426, 1992.
[99] Takeo Kanade, Masatoshi Okutomi. A stereo matching algorithm with an adaptive
window: Theory and experiment. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16, No.9, pp.920-932, 1994.
[100] Tinne Tuytelaars, Luc Van Gool. Wide baseline stereo matching based on local, affinely invariant regions. In Majid Mirmehdi and Barry Thomas, editors, Proceedings British Machine Vision Conference, Vol.2, pp.412-425, 2000.
[101] Yuri Boykov, Olga Veksler, Ramin Zabih. Fast approximate energy minimization via graph cuts. In Proceedings of International Conference on Computer Vision, Vol. 1, pp.377-384, September 1999.
[102] M. Trajkovic, M. Hedley, Fast comer detection, Image and Vision Computing 1998:(16) pp.75-87, 1998.
[103] Q. Zheng, R. Chellapa, A computational vision approach to image registration, IEEE Transactions on Image Processing 1993:(2), pp.311-325, 1993.
[104] U. R. Dhond, J. K. Aggarwal, Structure from Stereo-A Review, IEEE Trans. Systems, Man, and Cybernetics, Vol. 19, pp. 1489-1510, 1989.
[105] V. Komolgorov, R. Zabih, Computing Visual Correspondence with Occlusions via Graph Cuts, Proc. Int'l Conf. Computer Vision, Vol. Ⅱ, pp.508-515,2001.
[106] V. S. Kluth, G. W. Kunkel, U. A. Rauhala, Global Least Squares Matching, Proc. Int'l Geoscience and Remote Sensing Symp., Vol.2, pp. 1615-1618, 1992.
[107] V. Venkateswar, R. Chellappa, Hierarchical Stereo and Motion Correspondence Using Feature Groupings, Int'l J. Computer Vision, Vol. 15, pp.245-269, 1995.
[108] Vladimir Kolmogorov, Ramin Zabih. Computing visual correspondence with occlusions using graph cuts. In Proceedings of the 8th International Conference on Computer Vision, Vancouver, Canada, July, 2001.
[109] Vladimir Kolmogorov, Ramin Zabih. Multi-camera scene reconstruction via graph cuts. In Proceedings 7th European Conference on Computer Vision, Vol.3, pp.82-96, Copenhagen, Denmark, May 2002.
[110] T. Tuytelaars, L.V. Gool, Matching widely separated views based on affinely invariant neighbourhoods, International Journal of Computer Vision, Vol.59, No.1, 2004.
[111] W. Hoff, N. Ahuja, Surfaces from Stereo: Integrating Feature Matching, Disparity
Estimation, and Contour Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.11, No.2, pp.121-136, 1989.
[112] W. Y. Ma, B. S. Manjunath, Edge flow: a framework of boundary detection and image segmentation, Proc. of CVPR, pp.744-49, 1997.
[113] W. Forstner, E. Gulch, A fast operator for detection and precise location of distinct points, corners and centers of circular features, Proceedings of the ISPRS Workshop on Fast Processing of Photogrammetric Data, Switzerland, pp.251-305, 1986.
[114] Z. Zheng, H. Wang, E.K. Teoh, Analysis of gray level corner detection, Pattern Recognition Letters 1999:(20), pp. 149-162, 1999.
[115] Yuri Boykov, Olga Veksler, Ramin Zabih, Disparity component matching for visual correspondence., Proceedings of International Conference on Computer Vision and Pattern Recognition, pp.470-475, 1997.
[116] Y. Boykov, O. Veksler, R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.23, No.11, pp.1222-1239, 2001.
[117] Y. Boykov, V. Kolmogorov, An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision, Proc. Third Int'l Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, LNCS 2134, pp.359, Springer, 2001.
[118] Y. Deng, B. S. Manjunath, Unsupervised Segmentation of Color-Texture Regions in Images and Video, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.23, No.S, pp.800-810, 2001.
[119] Y. Nakamura, T. Matsuura, K. Satoh, Y. Ohta, Occlusion Detectable Stereo-Occlusion Patterns in Camera Matrix, Proc. Computer Vision and Pattern Recognition, pp.371-378, 1996.
[120] Y. Ohta, T. Kanade, Stereo by Intra- and Intra-Scan-line Search Using Dynamic Programming, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.7, pp.139-154, 1955.
[121] Y. Zhang, R. Webber, A Windowing Approach to Detecting Line Segments Using Hough Transform, Pattern Recognition, Vol.29, No.2, pp.255-266, Feb. 1996.
[122] F. Mindru, T. Moons, L. Van Gool, Recognizing color patterns irrespective of viewpoint and illumination, IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1, pp.368-373, 1999.
[123] 张引，潘云鹤，基于模拟退火的最大似然聚类图像分割算法，软件学报，第12卷，第2期，pp．212-218，2001年；
[124] 张永生，数字摄影测量[M]，北京：解放军出版社，1997年；
[125] 张祖勋，张剑清[M]，数字摄影测量学[M]，武汉测绘科技大学出版社，1997年；
[126] 章毓晋，图像理解与计算机视觉[M]，北京：清华大学出版社，1999年；
[127] 周新伦，柳健，刘华志，数字图像处理[M]，北京：国防工业出版社，1986年；
[128] 冯文灏，非地形摄影测量，北京：测绘出版社，1985年；
[129] 高满屯，曲仕茹，李西琴著，计算机视觉研究中的投影理论和方法[M]，西安：西北工业大学出版社，1998年4月；
[130] 高文，陈熙霖，计算机视觉[M]，北京：清华大学出版社，1999年；
[131] 何清法，李国杰，一种自动抽取图像中可判别区域的新方法，计算机学报，第25卷，第8期，pp．801-809，2002年；
[132] 胡占义，吴福朝，基于主动视觉摄像机标定方法[J]，计算机学报，第25卷，第11期，pp1149-1156，2002年；
[133] 黄建军，赵荣椿，基于模糊ART的图像分割，电子学报，第29卷，第5期，pp．718-720，2001年；
[134] 金为铣等，摄影测量学[M]，武汉：武汉测绘科技大学出版社，1996年；
[135] 李德仁等，摄影测量与遥感概论[M]，北京：测绘出版社，2001年；
[136] 李介谷，计算机视觉的理论与实践[M]，上海：上海交通大学出版社，1998年；
[137] 王宇宙，赵宗涛，一种多进小波变换DEM数据简化算法，计算机应用，第23卷，第6期，pp．22-23，2003年；
[138] 沈沛意，基于像素点转移图像合成的若干关键技术研究，西安电子科技大学博士学位论文，1999年；
[139] 石纯一，黄昌宁，人工智能原理[M]，北京：清华大学出版社，1995年；
[140] 沈邦乐，计算机图像处理[M]，北京：解放军出版社，1995年；
[141] 王磊，戚飞虎，图像分割的自适应FKCN方法，电子学报，第28卷，第2期，pp．4-6，2000年；
[142] 叶奕乾，祝蓓里编，心理学[M]，上海：华东师范大学出版社，1996年；
[143] 王宇宙，赵宗涛，一种三维地形绘制的细节简化方法，西北大学学报，第33卷，第5期，pp．527-530，2003年；
[144] 王宇宙，赵宗涛，王旭红，基于数学形态学的遥感图像分割，微电子学与计算机，第21卷，第5期，2004年；
[145] Richard Hartley,Andrew Zisserman著，韦穗译，计算机视觉中的多视图几何[M]，合肥：安徽大学出版社，2002年；
[146] 文贡坚，王润生，一种稳健的直线提取算法，软件学报，第12卷，第11期，pp．1660-1666，2001年。
[2] A. R. Mansouri, A. Mitiche, J. Konrad, Selective Image Diffusion: Application to Disparity Estimation, Proc. Int'l Conf. Image Processing, Vol.3, pp.284-288, 1998.
[3] Aaron F. Bobick, Stephen S. Intille. Large occlusion stereo. International Journal on Computer Vision, Vol.33, No.3, pp. 181-200, 1999.
[4] A. Goshtasby, G.C. Stockman, C.V. Page, A region-based approach to digital image registration with subpixel accuracy, IEEE Transactions on Geoscience and Remote Sensing, 1986(24), pp.390-399, 1986.
[5] B. D. Lucas, T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision, Proc. Int'l Joint Conf. A. I., pp.674-679, 1981.
[6] A.S. Vasileisky, B. Zhukov, M. Berger, Automated image coregistration based on linear feature recognition, Proceedings of the Second Conference Fusion of Earth Data, Sophia Antipolis, France, pp.59-66, 1998.
[7] B. Likar, F. Pernus, Automatic extraction of corresponding points for the registration of medical images, Medical Physics, 1999(26), pp. 1678-1686, 1999.
[8] C. Chang, S. Chatterjee, P. R. Kube, On an Analysis of Static Occlusion in Stereo Vision, Proc. Computer Vision and Pattem Recognition, pp. 722-723, 1991.
[9] C. Lawrence Zitnick, Takeo Kanade. A cooperative algorithm for stereo matching and occlusion detection. IEEE Transactions on Pattem Analysis and Machine Intelligence, Vol.22, No.7, pp.675-684, 2000.
[10] C. Schmid, A. Zisserman, A. Fitzgibbon, Automatic Line Matching and 3D Reconstruction of Buildings from Multiple Views, ISPRS Conference on Automatic Extraction of GIS Objects from Digital Imagery, IAPRS Vol.32, Part 3-2W5, pp.69-80, September 1999.
[11] C. Schmid, A. Zisserman, The Geometry and Matching of Curves in Multiple Views,
Proc. European Conf. Computer Vision, pp. 104-118, 1998.
[12] C. Silva, J. Santos-Victor, Intrinsic Images for Dense Stereo Matching with Occlusions, Proc. European Conf. Computer Vision, pp.100-114, 2000.
[13] C. Sun, A Fast Stereo Matching Method, Proc. Digital Image Computing: Techniques and Applications, pp.95-100, 1997.
[14] C. Tomasi, R. Manduchi, Stereo without Search, Technical Report STAN-CS-TR-95-1543, Stanford University, 1995.
[15] C. Tomasi, R. Manduchi, Stereo Matching as a Nearest-Neighbor Problem, IEEE Trans. Pattem Analysis and Machine Intelligence, Vol.20, pp.333-340, 1998.
[16] Carsten Rother and Stefan Carlsson, Linear Multi View Reconstruction and Camera Recovery, Proc. 8th Intemational Conference on Computer Vision, Vancouver, Canada, pp.42-49. 2001.
[17] D. Comaniciu, P. Meer, Mean Shift: A Robust Approach towards Feature Space Analysis, IEEE Transactions on Patter Analysis and Machine Intelligence, Vol.24, No.5, pp.603-619, May2002
[18] D. N. Bhat, S. K. Nayar, Ordinal Measures for Image Correspondence, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.20, pp.415-423, 1998.
[19] D. P. Robertson, R. Cipolla, Building Architectural Models from Many Views Using Map Constraints, Computer Vision-ECCV 2002, 7th European Conference on Computer Vision, Copenhagen, Denmark, Proceedings, Part Ⅱ. May 28-31, 2002.
[20] D. Scharstein, R. Szeliski, Stereo Matching with Non-Linear Diffusion, Int'l J. Computer Vision, Vol.28, No.2, pp. 155-174, 1998.
[21] D. Scharstein, R. Szeliski, A Taxonomy and Evaluation of Dense two-Frame Stereo Correspondence Algorithms, Int'l J. Computer Vision, Vol.47, No. 1, pp.7-42, 2002.
[22] D. H. Ballard, Generalizing the Hough Transform to Detect Arbitrary Shapes, Pattern Recognition, Vol.13, No.2, pp.111-122, 1981.
[23] B.S. Manjunath, C. Shekhar, R. Chellapa, A new approach to image feature detection with applications, Pattern Recognition, Vol.29, pp.627-640, 1996.
[24] F. Bigone, O. Henricsson, P. Fua, M. Stricker, Automatic Extraction of Generic House Roofs from High Resolution Aerial Imagery, Proc. European Conf. Computer Vision,
pp.85-96, 1996.
[25] H. Li, M. A. Lavin, R. J. LeMaster, Fast Hough Transform: A Hierarchical Approach, Computer Vision, Graphics, and Image Processing, Vol.36, No.2, pp. 139-161, 1986.
[26] H. P. Moravec, Towards automatic visual obstacle avoidance. Proceedings of the 5th International Joint Conference on Artificial Intelligence, Carnegie-Mellon University, Pittsburgh, PA, August 1977.
[27] H. Yamamoto, Y. Yeshurun, M. D. Levine. An active foveated vision system: Attentional mechanisms and scan path covergence measures. Journal of Computer Vision and Image Understanding, 63(1), pp.50-65, 1996.
[28] H. Zhao, Global Optimal Surface from Stereo, Proc. Int'l Conf. Pattern Recognition, Vol. 1, pp. 101-104, 2000.
[29] Hartley R, Self-calibration of stationary cameras, International Journal of Computer Vision, Vol.22, No. 1, pp.5-23, 1997.
[30] Heiko Hirschmuller. Improvements in real-time correlation-based stereo vision. In Proceedings of Workshop on Stereo and Multi-Baseline Vision, Kauai, Hawaii, pp.141-148, 2001.
[31] Heung-Yeung Shum, Mei Han, Richard Szeliski, Interactive Construction of 3D Models from Panoramic Mosaics, Proc. of CVPR'98, June, 1998.
[32] B. Zitova, J. Kautsky, G. Peters, J. Flusser, Robust detection of significant points in multiframe images, Pattern Recognition Letters 1999(20), pp. 199-206, 1999.
[33] I. J. Cox, S. L. Hingorani, S. B. Rao, B. M. Maggs, A Maximum Likelihood Stereo Algorithm, Computer Vision and Image Understanding, Vol.63, pp.542-567, 1996.
[34] I. Thomos, S. Malasiotis, M. G. Strintzis, Optimized Block Based Disparity Estimation in Stereo Systems Using a Maximum-Flow Approach, Proc. SIBGRAPI'98 Conf., 1998.
[35] J. B. Bums, A. Hanson, E. Riseman, Extracting straight lines., IEEE Transaction on Pattern Analysis and Machine Intelligent, Vol.8, No.4, pp.425-455.1986.
[36] J Cooper, S Venkatesh, L Kitchen. Early jump-out comer detections. IEEE Transactions on Pattern Analysis and Machine Intelligence. 15, pp.823-828, 1993
[37] J. J. Little, W. E. Gillett, Direct Evidence for Occlusion in Stereo and Motion, Image
and Vision Computing, Vol.8, No.4, pp.328-340, 1990.
[38] J. Shah, A Nonlinear Diffusion Model for Discontinuous Disparity and Half Occlusions in Stereo, Proc. Computer Vision and Pattern Recognition, pp.34-40, 1993.
[39] Jian Sun, Heung-Yeung Shum, Nan-Ning Zheng. Stereo matching using belief propagation. In Proceedings 7th European Conference on Computer Vision, Vol.2, pp.510-524, Copenhagen, Denmark, May 2002.
[40] J. W. Gates, M. Haseyama, H. Kitajima, A Real-time Line Extraction Method, IEEE International Symposium on Circuits and Systems'99, Vol. Ⅳ, pp.68-71, 1999.
[41] J. Woodfill, B. Von Herzen, Real-Time Stereo Vision on the PARTS Reconfigurable Computer, Proc. IEEE Workshop on FPGAs for Custom Computing Machines, pp.242-250, 1997.
[42] Masatoshi Okutomi, Yasuhiro Katayama, Setsuko Oka. A simple stereo algorithm to recover precise object boundaries and smooth surfaces. In Proceedings of Workshop on Stereo and Multi-Baseline Vision, pp. 158-165, 2001.
[43] Yonatan Wexler, Andrew W. Fitzgibbon, Andrew Zisserman, Learning epipolar geometry from image sequences, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2003.
[44] Joseph Giarratano, Gary Riley, Expert system principles and programming, PWS Publishing Company, 1998.
[45] K. N. Kutulakos, S. M. Seitz, A Theory of Shape by Space Carving, Int'l J. Computer Vision, Vol.38, No.3, pp.199-218, 2000.
[46] K. Muhlmann, D. Maier, J. Hesser, R. Manner, Calculating Dense Disparity Maps from Color Stereo Images, an Efficient Implementation, Proc. IEEE Workshop on Stereo and Multi-Baseline Vision, pp.30-36, 2001.
[47] K. Satoh, Y. Ohta, Occlusion Detectable Stereo-Systematic Comparison of Detection Algorithms, Proc. Int'l Conf. Pattern Recognition, pp.280-286, 1996.
[48] L. Matthies, A. Kelly, T. Litwin, G. Tharp, Obstacle Detection for Unmanned Ground Vehicles: A Progress Report, Proc. Intelligent Vehicles'95 Symp., pp.66-71, 1995.
[49] L. Shafarenko, M. Petrou, Automatic watershed segmentation of randomly textured color images. IEEE Transactions on Image Processing, Vol.6, No.11, pp.1530-44, 1997.
[50] L. Xu, E. Oja, Randomized Hough Transform (RHT)-Basic Mechanisms, Algorithms, and Computational Complexities, CVGIP-Image Understanding, Vol.57, No.2, pp.131-154, 1993.
[51] M. Boldt, R. Weiss, E. Riseman, Token-based extraction of straight lines, IEEE Transaction on System, Man Cybernetics, Vol. 19, No.7, pp. 1581-1594. 1989.
[52] M. K. Reed, P. K. Allen, Constraint-Based Sensor Planning for Scene Modeling, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.22, pp. 1460-1467, 2000.
[53] M. Pilon, P. Cohen, Occlusion Detection and Interpretation Based on Image Reprojection, Proc. S. W. Symp. Image Analysis and Interpretation, pp. 166-171, 1996.
[54] M. Tistarelli, G. Sandini. On the advantages of polar and log-polar mapping for direct estimation of time-to-impact from optical flow, IEEE Transaction on PAMI, Vol.15, No.4, pp.401-410, 1993.
[55] M. Z. Brown, D. Burschka, G. D. Hager, Advances in Computational Stereo, Vol.25, No.8, pp.993-1008, 2003.
[56] D. Bhattacharya, S. Sinha, Invariance of stereo images via theory of complex moments, Pattern Recognition (30) pp. 1373-1386. 1997.
[57] Marc Bolduc, Martin D. Levine, A Review of Biologically-Motivated Space-Variant Data Reduction Models, Computer Vision and Image Understanding, Vol. 69, No. 2, pp.170-184, 1998.
[58] Matthew E. Antone, Seth Teller, Automatic Recovery of Relative Camera Rotations for Urban Scenes, Computer Vision and Pattern Recognition, CVPR'OO, Vol.2, pp.2282, June, 2000.
[59] H.S. Alhichri, M. Kamel, Virtual circles: a new set of features for fast image registration, Pattem Recognition Letters (24), pp. 1181-1190. 2003.
[60] J. Matas, S. Obdrzalek, O. Chum, Local affine frames for widebaseline stereo, R. Kasturi, D. Laurendeau, C. Suen (Eds.), 16th International Conference on Pattern Recognition ICPR 2002, Vol.4, pp.363-366. 2002.
[61] Minglun Gong, Yee-Hong Yang. Multi-resolution stereo matching using genetic algorithm, In Proceedings of Workshop on Stereo and Multi-Baseline Vision, pp.21-29, Kauai, Hawaii, December 2001.
[62] O. Chutatape, L. Guo, A Modified Hough Transform for Line Detection and its Performance, Pattern Recognition, Vol.32, No.2, pp. 181-182, Feb. 1999.
[63] K. Rohr, Landmark-Based Image Analysis: Using Geometric and Intensity Models, Computational Imaging and Vision Series, Vol.21, Kluwer Academic Publishers, Dordrecht, 2001.
[64] O. Faugeras, et al, Real Time Correlation-Based Stereo: Algorithm, Implementations and Applications, INRIA Technical Report 2013, 1993.
[65] O. Faugeras, Q.-T. Luong, The Geometry of Multiple Images, The MIT Press, Cambridge, Massachusetts, 2001.
[66] O. Zuniga, R. M. Haralick. Comer detection using the facet model, Computer Vision and Pattern Recognition, pp.30-37, IEEE, Los Alamitos, CA, 1983.
[67] P. Aschwanden, W. Guggenbuhl, Experimental Results from a Comparative Study on Correlation Type Registration Algorithms, In Robust Computer Vision, Wickmann, pp.268-289, 1993.
[68] Paul Beardsley, Phil Torr, Andrew Zisserman, 3D Model Acquisition from Extended Image Sequences, Proc. 4th European Conference on Computer Vision, Cambridge; LNCS 1065, Vol. Ⅱ, pp.683-685, Springer-Verlag, 1996.
[69] P. Corke, P. Dunn, Real-Time Stereopsis Using FPGAs, Proc. IEEE TENCON-Speech and Image Technologies for Computing and Telecom, pp.235-238, 1997.
[70] P. Fua, A Parallel Stereo Algorithm that Produces Dense Depth Maps and Preserves Image Features, Machine Vision and Applications, Vol.6, pp.35-49, 1993.
[71] P. Fua, Y. G. Leclerc, Object-Centered Surface Reconstruction: Combining Multi-image Stereo and Shading, Int'l J. Computer Vision, Vol. 16, pp.35-56, 1995.
[72] P. N. Belhumeur, A Bayesian Approach to Binocular Stereopsis, Int'l J. Computer Vision, Vol.19, No.3, pp.237-260, 1996.
[73] Pollefeys M, Van Gool L, A stratified approach to metric self-calibration, Proc IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, California, pp.407-412, 1997.
[74] Pollefeys M, Koch R, Van Gool L., Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters. In: Proc International Conference on Computer Vision, Bombay, India, pp.90-95, 1998.
[75] R. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, Cambridge, United Kingdom, 2000.
[76] R. Nelson, Finding line segments by stick growing, IEEE Transaction on Pattern Analysis and Machine Intelligent, Vol. 16, No.5, pp.519-523. 1994.
[77] R. Wallace, P. Wen Ong, B. Bederson, E. Schwartz. Space variant image processing. International Journal of Computer Vision, Vol. 13, No. 1, pp.71-90, 1994.
[78] R. Wildes, Direct Recovery of Three-Dimensional Scene Geometry from Binocular Stereo Disparity, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 13, No.8, pp.761-774, 1991.
[79] R. Zabih, J. Woodfill, Non-Parametric Local Transforms for Computing Visual Correspondence, Proc. 3rd European Conf. Computer Vision, pp. 150-158, 1994.
[80] R. Sara, R. Bajcsy, On Occluding Contour Artifacts in Stereo Vision, Proc. Computer Vision and Pattern Recognition, pp.852-857, 1997.
[81] D. Tell, S. Carlsson, Wide baseline point matching using affine invariants computed from intensity profiles, European Conference on Computer Vision, Vol. 1, pp.814-828, 2000.
[82] Radim Sara, Finding the largest unambiguous component of stereo matching, In Proceedings 7th European Conference on Computer Vision, Vol.3, pp.900-914, 2002.
[83] Richard Szeliski, Daniel Scharstein, Symmetric sub-pixel stereo matching, In Proceedings 7th European Conference on Computer Vision, Vol.2, pp.525-540, 2002.
[84] P. Pritchett, A. Zisserman, Wide Baseline Stereo, Int'l Conference on Computer Vision, pp.754-759, 1998.
[85] S. Belongie, C. Carson, Color-and texture-based image segmentation using EM and its application to content-based image retrieve, Proc. of ICCV, pp.675-82, 1998.
[86] S. Birchfield, C. Tomasi, Depth Discontinuities by Pixel-to-Pixel Stereo, Proc. IEEE Int'l Conf. Computer Vision, pp.1073-1080, 1998
[87] S. Birchfield, C. Tomasi, Multiway Cut for Stereo and Motion with Slanted Surfaces, Proc. Int'l Conf. Computer Vision, Vol. 1, pp.489-495, 1999
[88] S. D. Cochran, G. Medioni, 3-D Surface Description from Binocular Stereo, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 14, No. 10, pp.981-994, 1992.
[89] S. Kimura, T. Shinbo, H. Yamaguchi, E. Kawamura, K. Naka, A Convolve-Based Real-Time Stereo Machine (SAZAN), Proc. Computer Vision and Pattern Recognition, Vol. 1, pp.457-463, 1999.
[90] S. M. Smith, J. M. Brady, SUSAN-A New Approach to Low Level Image Processing. Int. Journal of Computer Vision, Vol.23, No.1, pp.45-78, May, 1997.
[91] S. Randriamasy, A. Gagalowicz, Region Based Stereo Matching Oriented Image Processing, Proc. Computer Vision and Pattern Recognition, pp.736-737, 1991.
[92] S. Roy, I. J. Cox, A Maximum-Flow Formulation of the N-camera Stereo Correspondence Problem, Proc. Int'l Conf. Computer Vision, pp.492-499, 1998.
[93] S.S. Intille, A. F. Bobick, Incorporating Intensity Edges in the Recovery of Occlusion Regions, Proc. Int'l Cone Pattern Recognition, Vol. 1, pp.674-677, 1994.
[94] S. Scherer, P. Werth, A. Pinz, The Discriminatory Power of Ordinal Measures-Towards a New Coefficient, Proc. Computer Vision and Pattern Recognition, Vol. 1, pp.76-81, 1999.
[95] S. T. Bamard, M. A. Fischler, Computational Stereo, ACM Computing Surveys, Vol.14, pp.553-572, 1982.
[96] Ye Zhang, Chandra Kambhamettu. Stereo matching with segmentation-based cooperation. In Proceedings 7th European Conference on Computer Vision, Vol. 2, pp.556-571, Copenhagen, Denmark, May 2002.
[97] Sebastien Roy. Stereo without epipolar lines: A maximum-flow formulation. International Journal of Computer Vision, Vol.34, No.3, pp.147-161, 1999.
[98] T. Kanade, M. Okutomi, T. Nakahara, A Multiple-baseline Stereo Method, Proc. ARPA Image Understanding Workshop, pp.409-426, 1992.
[99] Takeo Kanade, Masatoshi Okutomi. A stereo matching algorithm with an adaptive
window: Theory and experiment. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16, No.9, pp.920-932, 1994.
[100] Tinne Tuytelaars, Luc Van Gool. Wide baseline stereo matching based on local, affinely invariant regions. In Majid Mirmehdi and Barry Thomas, editors, Proceedings British Machine Vision Conference, Vol.2, pp.412-425, 2000.
[101] Yuri Boykov, Olga Veksler, Ramin Zabih. Fast approximate energy minimization via graph cuts. In Proceedings of International Conference on Computer Vision, Vol. 1, pp.377-384, September 1999.
[102] M. Trajkovic, M. Hedley, Fast comer detection, Image and Vision Computing 1998:(16) pp.75-87, 1998.
[103] Q. Zheng, R. Chellapa, A computational vision approach to image registration, IEEE Transactions on Image Processing 1993:(2), pp.311-325, 1993.
[104] U. R. Dhond, J. K. Aggarwal, Structure from Stereo-A Review, IEEE Trans. Systems, Man, and Cybernetics, Vol. 19, pp. 1489-1510, 1989.
[105] V. Komolgorov, R. Zabih, Computing Visual Correspondence with Occlusions via Graph Cuts, Proc. Int'l Conf. Computer Vision, Vol. Ⅱ, pp.508-515,2001.
[106] V. S. Kluth, G. W. Kunkel, U. A. Rauhala, Global Least Squares Matching, Proc. Int'l Geoscience and Remote Sensing Symp., Vol.2, pp. 1615-1618, 1992.
[107] V. Venkateswar, R. Chellappa, Hierarchical Stereo and Motion Correspondence Using Feature Groupings, Int'l J. Computer Vision, Vol. 15, pp.245-269, 1995.
[108] Vladimir Kolmogorov, Ramin Zabih. Computing visual correspondence with occlusions using graph cuts. In Proceedings of the 8th International Conference on Computer Vision, Vancouver, Canada, July, 2001.
[109] Vladimir Kolmogorov, Ramin Zabih. Multi-camera scene reconstruction via graph cuts. In Proceedings 7th European Conference on Computer Vision, Vol.3, pp.82-96, Copenhagen, Denmark, May 2002.
[110] T. Tuytelaars, L.V. Gool, Matching widely separated views based on affinely invariant neighbourhoods, International Journal of Computer Vision, Vol.59, No.1, 2004.
[111] W. Hoff, N. Ahuja, Surfaces from Stereo: Integrating Feature Matching, Disparity
Estimation, and Contour Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.11, No.2, pp.121-136, 1989.
[112] W. Y. Ma, B. S. Manjunath, Edge flow: a framework of boundary detection and image segmentation, Proc. of CVPR, pp.744-49, 1997.
[113] W. Forstner, E. Gulch, A fast operator for detection and precise location of distinct points, corners and centers of circular features, Proceedings of the ISPRS Workshop on Fast Processing of Photogrammetric Data, Switzerland, pp.251-305, 1986.
[114] Z. Zheng, H. Wang, E.K. Teoh, Analysis of gray level corner detection, Pattern Recognition Letters 1999:(20), pp. 149-162, 1999.
[115] Yuri Boykov, Olga Veksler, Ramin Zabih, Disparity component matching for visual correspondence., Proceedings of International Conference on Computer Vision and Pattern Recognition, pp.470-475, 1997.
[116] Y. Boykov, O. Veksler, R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.23, No.11, pp.1222-1239, 2001.
[117] Y. Boykov, V. Kolmogorov, An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision, Proc. Third Int'l Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, LNCS 2134, pp.359, Springer, 2001.
[118] Y. Deng, B. S. Manjunath, Unsupervised Segmentation of Color-Texture Regions in Images and Video, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.23, No.S, pp.800-810, 2001.
[119] Y. Nakamura, T. Matsuura, K. Satoh, Y. Ohta, Occlusion Detectable Stereo-Occlusion Patterns in Camera Matrix, Proc. Computer Vision and Pattern Recognition, pp.371-378, 1996.
[120] Y. Ohta, T. Kanade, Stereo by Intra- and Intra-Scan-line Search Using Dynamic Programming, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.7, pp.139-154, 1955.
[121] Y. Zhang, R. Webber, A Windowing Approach to Detecting Line Segments Using Hough Transform, Pattern Recognition, Vol.29, No.2, pp.255-266, Feb. 1996.
[122] F. Mindru, T. Moons, L. Van Gool, Recognizing color patterns irrespective of viewpoint and illumination, IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1, pp.368-373, 1999.
[123] 张引，潘云鹤，基于模拟退火的最大似然聚类图像分割算法，软件学报，第12卷，第2期，pp．212-218，2001年；
[124] 张永生，数字摄影测量[M]，北京：解放军出版社，1997年；
[125] 张祖勋，张剑清[M]，数字摄影测量学[M]，武汉测绘科技大学出版社，1997年；
[126] 章毓晋，图像理解与计算机视觉[M]，北京：清华大学出版社，1999年；
[127] 周新伦，柳健，刘华志，数字图像处理[M]，北京：国防工业出版社，1986年；
[128] 冯文灏，非地形摄影测量，北京：测绘出版社，1985年；
[129] 高满屯，曲仕茹，李西琴著，计算机视觉研究中的投影理论和方法[M]，西安：西北工业大学出版社，1998年4月；
[130] 高文，陈熙霖，计算机视觉[M]，北京：清华大学出版社，1999年；
[131] 何清法，李国杰，一种自动抽取图像中可判别区域的新方法，计算机学报，第25卷，第8期，pp．801-809，2002年；
[132] 胡占义，吴福朝，基于主动视觉摄像机标定方法[J]，计算机学报，第25卷，第11期，pp1149-1156，2002年；
[133] 黄建军，赵荣椿，基于模糊ART的图像分割，电子学报，第29卷，第5期，pp．718-720，2001年；
[134] 金为铣等，摄影测量学[M]，武汉：武汉测绘科技大学出版社，1996年；
[135] 李德仁等，摄影测量与遥感概论[M]，北京：测绘出版社，2001年；
[136] 李介谷，计算机视觉的理论与实践[M]，上海：上海交通大学出版社，1998年；
[137] 王宇宙，赵宗涛，一种多进小波变换DEM数据简化算法，计算机应用，第23卷，第6期，pp．22-23，2003年；
[138] 沈沛意，基于像素点转移图像合成的若干关键技术研究，西安电子科技大学博士学位论文，1999年；
[139] 石纯一，黄昌宁，人工智能原理[M]，北京：清华大学出版社，1995年；
[140] 沈邦乐，计算机图像处理[M]，北京：解放军出版社，1995年；
[141] 王磊，戚飞虎，图像分割的自适应FKCN方法，电子学报，第28卷，第2期，pp．4-6，2000年；
[142] 叶奕乾，祝蓓里编，心理学[M]，上海：华东师范大学出版社，1996年；
[143] 王宇宙，赵宗涛，一种三维地形绘制的细节简化方法，西北大学学报，第33卷，第5期，pp．527-530，2003年；
[144] 王宇宙，赵宗涛，王旭红，基于数学形态学的遥感图像分割，微电子学与计算机，第21卷，第5期，2004年；
[145] Richard Hartley,Andrew Zisserman著，韦穗译，计算机视觉中的多视图几何[M]，合肥：安徽大学出版社，2002年；
[146] 文贡坚，王润生，一种稳健的直线提取算法，软件学报，第12卷，第11期，pp．1660-1666，2001年。