政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/61488

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/61488

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文筆數/總筆數 : 113648/144635 (79%)
造訪人次 : 51663536 線上人數 : 515

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜尋範圍

查詢小技巧：

您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果

若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料

進階搜尋

主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於政大典藏 ‧ 管理

到手機版

政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/61488

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/61488

題名:	多視角影像中的退化問題與參數估測 Degeneracies and Parameter Estimation in Multiple View Images
作者:	詹凱軒 Chan, Kai Hsuan
貢獻者:	何瑁鎧郭正佩唐政元 Hor, Maw Kae Kuo, Pei Jeng Tang, Cheng Yuan 詹凱軒 Chan, Kai Hsuan
關鍵詞:	退化參數估測多視角幾何緊要配置粒子群最佳化貼片匹配法 degeneracy parameter estimation multiple view geometry critical configuration particle swarm optimization patch-based matching
日期:	2013
上傳時間:	2013-11-01 11:43:28 (UTC+8)
摘要:	多視角影像比起單一影像，可提供更多資訊，有助於影像之分析、比對與建模。相關研究諸如利用多視角影像進行三維模型重建、影像拼接、影像修補、利用多視角攝影機進行物體與人物追蹤、與四維動態模型捕捉等。然而，多視角影像的研究中，自動且精確的偵測影像對應點一直是個困難的問題。其中多視角影像的退化(degeneracy)問題，經常被研究者所忽略，而此一問題往往造成幾何關係上錯誤的估算，影響後續處理的精確度。本研究中我們所定義之退化問題涵蓋範圍較廣，包含傳統數學上的退化，以及同時考慮多視角幾何與影像紋路匹配所造成之問題，我們將這些問題歸納成三種類型並進行探討：第一種類型，當相機參數未知、且影像幾何關係也未估算時，需透過對應點進行影像幾何的計算，若這些對應點相對之三維座標與相機中心之配置，剛好落在特定曲面或平面之情況，則會造成錯誤的估算；第二種類型，當相機參數已知、或已估算求得影像幾何關係時，透過這些估算的幾何關係，進行對應點的轉換，在特定情況下也會造成錯誤的結果；第三種類型，當我們透過多視角幾何關係，結合影像紋路，利用貼片(patch-based)方法進行對應點相似度評估時，容易因為視角的差異、比例的差異、及貼片平面與物體表面的差異，造成無法正確評估之問題。我們針對這三種類型的退化問題，分別進行分析與探討，並提供適當的建議與處理方法，以避開可能遇到的退化狀況。同時，無論是在對應點的估算、多視角三維重建、與多視角幾何的評估等，通常需要透過強健的參數估測方法來求得，我們也分析、討論近年許多代表性的參數估測方法，並提出更穩健的估測方法，應用於處理特定退化問題。透過我們提出的退化規避方法，能有效的提高多視角影像處理與重建三維模型的精確度。 Multiple view images carry much more information than that in single view images. This information is helpful in analyzing, matching, and reconstruction the points and models in real scenes. There are many related researches such as three-dimensional model reconstruction from multi-view images, multi-view image stitching, multi-view image inpainting, multi-view object and human tracking as well as four-dimensional dynamic model capture, etc. However, automatic and accurate corresponding point matching is one of the difficult problems in multiple view researches. Moreover, the multi-view degeneracy problems are normally ignored by researchers. These problems will include geometric estimation error and cause inaccuracy in subsequently processing. In this dissertation, the definition of the degeneracy covers a wider scope. It includes the traditional degeneracy as discussed in mathematics as well as the corresponding point matching error caused by the model inaccuracy in geometries and in textures. We classify these problems into three categories and provide methods and guidelines for avoiding the degeneracy problems in multi-view image processing. The first category assumes the camera parameters or the geometries (for example, the fundamental matrix) are unknown and which must be estimated using the corresponding points. If the 3D locations of the corresponding points and the camera centers fall into particular configurations, such as the ruled quadric, the camera parameters estimation may yield multiple solutions or erroneous results. The second category assumes the camera geometry is known or known geometries (for example, the fundamental matrix or the trifocal tensor) are used to transfer the corresponding points. It may cause erroneous estimation under certain conditions. The third category occurs if we use the patch-based method to estimate the similarity of the corresponding points in multiple views, it will cause unreasonable estimation due to different viewing angle, image scaling, or inconsistencies between the patch plane and the object surface. We propose various guidelines as well as processing methods in order to avoid the degeneracies. In addition, it generally requires robust parameter estimation methods to accomplish for the corresponding point estimations, the 3D reconstructions from multiple views, as well as for the multi-view geometries estimations. We analyzed the most representative parameter estimation methods developed in recent years and proposed the more robust methods that can be used to handle the specific degeneracy problems. Using the proposed methods and guidelines for degeneracy avoidance, we can effectively improve the accuracy of the multi-view image processing as well as 3D model reconstructions.
參考文獻:	[1] Agarwal, S., Y. Furukawa, N. Snavely, B. Curless, S.M. Seitz, and R. Szeliski, Reconstructing Rome, IEEE Computer Society, Vol. 43, pp. 40-47, 2010. [2] Agarwal, S., Y. Furukawa, N. Snavely, B. Curless, S. M. Seitz and R. Szeliski, Building Rome in a Day. Communications of the ACM, Vol. 54, No. 14, pp. 105-112, 2011. [3] Armangué, X. and J. Salvi, Overall View Regarding Fundamental Matrix Estimation. Image and Vision Computing, Vol. 21, No. 2, pp. 205-220, 2003. [4] Barjatya, A., Block Matching Algorithm for Motion Estimation. DIP 6620 Final Project Paper, 2004. [5] Barnard, S. T. and M.A. Fischler, Computational Stereo. ACM Computing Surveys, Vol. 14, pp. 553-572, 1982. [6] Beck, J.V. and K.J. Arnold, Parameter Estimation in Engineering and Science. Wiley series in probability and mathematical statistics, Wiley, New York, 1977. [7] Bouguet, J.Y., Camera Calibration Toolbox for Matlab, 2010, Online URL <http://www.vision.caltech.edu/bouguetj/calib_doc/> [8] Bradley, D., T. Boubekeur, and W. Heidrich, Accurate Multi-View Reconstruction Using Robust Binocular Stereo and Surface Meshing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008. [9] Brahmachari, A.S., S. Sarkar, BLOGS: Balanced Local and Global Search for Non-Degenerate Two View Epipolar Geometry. In Proceedings of the Twelfth International Conference on Computer Vision, Kyoto, Japan 2009. [10] Brown, M. and D. Lowe, Automatic Panoramic Image Stitching Using Invariant Features. In International Journal of Computer Vision, Vol. 74, No. 1, pp. 59-77, 2007. [11] Brown, M. and D. G. Lowe, Unsupervised 3D Object Recognition and Reconstruction in Unordered Datasets. International Conference on 3-D Digital Imaging and Modeling, 2005. [12] Campbell, N.D.F., G. Vogiatzis, C. Hernández, and R. Cipolla, Automatic 3D Object Segmentation in Multiple Views Using Volumetric Graph-Cuts. Image and Vision Computing, Vol. 28, pp. 14-25, 2008. [13] Campbell, N.D.F., G. Vogiatzis, C. Hernández, and R. Cipolla, Using Multiple Hypotheses to Improve Depth-Maps for Multi-View Stereo. In Proceedings of the European Conference of Computer Vision, pp. 766-779, 2008. [14] Cernuschi-Frias, B., Cooper, D. B., Hung, Y.-P., Belhumeur, P. N.,1989. Toward a Model-Based Bayesian Theory for Estimating and Recognizing Parameterized 3-D Objects Using Two or More Images Taken from Different Positions. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, 1028-1052. [15] Chai, J. and S.D. Ma, Robust epipolar geometry estimation using genetic algorithm. Pattern Recognition Letters. Vol. 19, No. 9, pp. 829-838, 1998. [16] Chan, K.-H., C.-Y. Tang, M.-K. Hor, and Y.-L. Wu, Robust trifocal tensor constraints for structure from motion estimation. Pattern Recognition Letters, Vol. 34, No. 6, pp. 627-636, 2013. [17] Chan, K.-H., C.-Y. Tang, Y.-L. Wu, and M.-K. Hor, Robust Orthogonal Particle Swarm Optimization for Estimating the Fundamental Matrix, In Proceedings of IEEE Visual Communications and Image Processing, Tainan, Taiwan, November 2011. [18] Chum, O. and J. Matas, Matching with PROSAC - Progressive Sample Consensus. In Conference on Computer Vision and Pattern Recognition, pp. 220-226, 2005. [19] Connor, K. and I. Reid, Novel view specification and synthesis. In Proceeding of the British Machine Vision Conference, 2002. [20] Dhond, U. R. and J. K. Aggarwal, Structure from Stereo: a Review. IEEE Transactions on System, Man, and Cybernetics, Vol. 19, pp. 1489-1510, 1989. [21] Durrant-Whyte, H. and T. Bailey, Simultaneous localization and mapping: part I. IEEE Robotics and Automation Magazine, Vol. 13, No. 2, pp. 99-108, 2006. [22] Eberhart, R. and Y. Shi, Particle Swarm Optimization: Developments, Applications and Resources. In Proceedings of the 2001 Congress on Evolutionary Computation, pp. 81-86, 2001. [23] Faugeras, O. and B. Mourrain, About the Correspondences of Points Between N Images. In Proceedings of the IEEE Workshop on Representation of Visual Scenes, pp. 37–44, 1995. [24] Faugeras, O.D., L. Quan, and P. Sturm, Self-calibration of a 1D Projective Camera and its Application to the Self-calibration of a 2D Projective Camera. In Proceeding of the European Conference on Computer Vision, Vol. 1, pp. 36-52, 1998. [25] Faugeras, O. D., Q. Luong, and S. Maybank,. Camera self-calibration: Theory and experiments. In Proceeding of the European Conference on Computer Vision, pp. 321-334, 1992. [26] Feng, C.L. and Y.S. Hung, A Robust Method for Estimating the Fundamental Matrix. In Proceedings of Conference on Digital Image Computing: Techniques and Applications, pp. 633-642, 2003. [27] Fischler, M. A. and R. C. Bolles, Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, Vol. 24, pp. 381-395, 1981. [28] Forsyth, D. A. and J. Ponce, Computer Vision A Modern Approach. Second Edition, Prentice Hall, New Jersey, 2011. [29] Föstner, W., Reliability Analysis of Parameter Estimation in Linear Models with Application to Mensuration Problems in Computer Vision. Computer Vision, Graphics and Image Processing, Vol. 40, No. 3, pp. 273-310, 1987. [30] Frahm, J.-M., P. Fite-Georgel, D. Gallup, T. Johnson, R. Raguram, C. Wu, Y.-H. Jen, E. Dunn, B. Clipp, S. Lazebnik, and M. Pollefeys, Building Rome on a Cloudless Day. In Proceedings of the European Conference on Computer Vision, pp. 368–381, 2010. [31] Furukawa, Y. and J. Ponce, Accurate Camera Calibration from Multi-view Stereo and Bundle Adjustment. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008. [32] Furukawa, Y. and J. Ponce, Accurate, Dense, and Robust Multi-view Stereopsis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007. [33] Furukawa, Y. and J. Ponce, High-fidelity image-based modeling. Technical Report, UIUC, 2006. [34] Furukawa, Y., B. Curless, S.M. Seitz, and R. Szeliski, Towards Internet-scale Multi-view Stereo. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1434-1441, 2010. [35] Furukawa, Y., Patch-based Multi-view Stereo Software, 2010, Online URL < http://grail.cs.washington.edu/software/pmvs/>. [36] Gao, W., Robot Vision Group Data Set, 2011. Online URL < http://vision.ia.ac.cn/index.htm>. [37] Goesele, M., N. Snavely, B. Curless, H. Hoppe, and S.M. Seitz, Multi-View Stereo for Community Photo Collections. In Proceedings of the IEEE International Conference on Computer Vision, pp. 1-8, 2007. [38] Hartley, R. I. and A. Zisserman, Multiple View Geometry in Computer Vision, Second Edition, Cambridge University Press, 2004. [39] Hartley, R. I., and F. Kahl, A Critical Configuration for Reconstruction from Rectilinear Motion. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 511–517, 2003. [40] Hartley R. I. and F. Kahl, Critical Configurations for Projective Reconstruction from Multiple Views. International Journal of Computer Vision, Vol. 71, No. 1, pp.5-47, 2006. [41] Hedayat, A. S., N. J. A. Sloane, and J. Stufken, Orthogonal Arrays: Theory and Applications. New York: Springer-Verlag, 1999. [42] Heinrich, S. B. and W.E. Snyder, Internal Constraints of the Trifocal Tensor. In Proceedings of CoRR, 2011. [43] Hiep, V.H., R. Keriven, P. Labatut, and J.P. Pons, Towards High-resolution Large-scale Multi-view Stereo. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1430-1437, 2009. [44] Ho, S.-Y., H.-S. Lin, W.-H. Liauh, and S.-J. Ho, OPSO: Orthogonal Particle Swarm Optimization and Its Application to Task Assignment Problems. IEEE Transactions on Systems, Man, and Cybernetics—Part a: Systems and Humans, Vol. 38, No. 2, pp. 288-298, 2008. [45] Hor, M.-K., C.-Y. Tang, Y.-L. Wu, K.-H. Chan, and J.-J. Tsai, Robust Refinement Methods for Camera Calibration and 3D Reconstruction from Multiple Images. Pattern Recognition Letters, Vol. 32, No. 8, pp.1210-1221, 2011. [46] Hor, M.-K., and K.-H. Chan, and C.-Y. Tang, 3D Model Reconstruction Refinement from Multiple Images. International Conference on Multimedia Technology, 2011. [47] Hor, M.-K., W.-C. Chen, C.-Y. Tang, Y.-L. Wu, K.-H. Chan, and K.-S. Wu, Refinement of 3D Models Reconstructed from Visual Hull. International Display Manufacturing Conference/3D Display System and Application/Asia Display, 2009. [48] Hor, M.-K., W.-C. Chen, C.-Y. Tang, Y.-L. Wu, K.-H. Chan, and J.-J. Tsai, Using 3D Patches for Refinement of 3D Reconstruction from Multiple Images. International Display Manufacturing Conference/3D Display System and Application/Asia Display, 2009. [49] Hor, M.-K., W.-C. Chen, C.-Y. Tang, Y.-L. Wu, K.-H. Chan, and J.-Y. Tsai, Generation of Dense Image Matching Using Epipolar Geometry. International Display Manufacturing Conference/3D Display System and Application/Asia Display, 2009. [50] Hu, M., B. Yuan and X. Tang, Robust estimation of trifocal tensor using messy genetic algorithm. Chinese Journal of Electronics, Vol. 12, pp. 174-178, 2003. [51] Huang, J. -F., S. -H. Lai, C. -M. Cheng, Robust Fundamental Matrix Estimation with Accurate Outlier Detection. Journal of Information Science and Engineering. Vol. 23, No. 4, pp. 1213-1226, 2007. [52] Hung, Y.-P., Cooper, D. B., Cernuschi-Frias, B., Asymptotic Bayesian Surface Estimation Using an Image Sequence. International Journal of Computer Vision, Vol. 6, pp. 105-132, 1991. [53] Kennedy, J. and R. C. Eberhart, Particle Swarm Optimization. IEEE International Conference on Neural Networks, pp. 1942-1948, 1995. [54] Kim, K. and L.S. Davis, Multi-camera Tracking and Segmentation of Occluded People on Ground Plane Using Search-guided Particle Filtering. In Proceedings of European Conference on Computer Vision, Vol. 3953, pp.98-109, 2006. [55] Levoy, M., Stanford Spherical Gantry, 2002, Online URL <http://graphics.stanford.edu/projects/gantry/>. [56] Li, J., E. Li, Y. Chen, L. Xu, and Y. Zhang, Bundled Depth-map Merging for Multi-view Stereo. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2769-2776, 2010. [57] Li, R., B. Zeng, and M. L. Liou, A New Three-Step Search Algorithm for Block Motion Estimation. IEEE Transactions Circuits And Systems For Video Technology, Vol. 4., No. 4, pp. 438-442, 1994. [58] Liebowitz, D., Camera Calibration and Reconstruction of Geometry from Images, PhD thesis, University of Oxford, Dept. Engineering Science, 2001. [59] Liebowitz, D. and A. Zisserman, Combining scene and auto-calibration constraints, In Proceedings of the 7th International Conference on Computer Vision, September 1999. [60] Lowe, D. G., Distinctive Image Features from Scale-invariant Keypoints. International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004. [61] Lu J. and M.L. Liou, A Simple and Efficent Search Algorithm for Block-Matching Motion Estimation. IEEE Transactions Circuits And Systems For Video Technology, Vol. 7, No. 2, pp. 429-433, 1997. [62] MacQueen, J. B., Some Methods for Classification and Analysis of Multivariate Observations, In Proceedings of the 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, pp. 281-297, 1967. [63] Montemerlo, M., S. Thrun D. Koller, and B. Wegbreit, FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges. In Proceeding of the International Conference on Artificial Intelligence, 2003. [64] Moreels, P. and P. Perona, Evaluation of Features Detectors and Descriptors based on 3D Objects. International Journal of Computer Vision, Vol. 73, No. 3, pp.263–284, 2007. [65] Ni, K., H. Jin, and F. Dellaert, GroupSAC: Efficient Consensus in the Presence of Groupings, In Proceedings of the Twelfth International Conference on Computer Vision, 2009. [66] Nie, Y., and K.-K. Ma, Adaptive Rood Pattern Search for Fast Block-Matching Motion Estimation. IEEE Transcations Image Processing, Vol.11, No.12, pp.1442-1448, 2002. [67] Okutami, M. and T. Kanade, A Multiple-Baseline Stereo System. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, pp. 353–363, 1993. [68] Quan, L. and Z. Lan, Linear N-Point Camera Pose Determination. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, pp. 774-780, 1999. [69] Piater, J., Multi-Camera People Tracking by Collaborative Particle Filters and Principal Axis-Based Integration. In Asian Conference on Computer Vision, pp.365–374, 2007. [70] Po, L.-M., and W.-C. Ma, A Novel Four-Step Search Algorithm for Fast Block Motion Estimation. IEEE Transcations Circuits And Systems For Video Technology, Vol.6, No. 3, pp. 313-317, 1996. [71] Raguram, R., J. Frahm, and M. Pollefeys, A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus. In Proceedings of the European Conference on Computer Vision, pp. 500-513, 2008. [72] Ratnaweera, A., S. K. Halgamuge, and H. C. Watson, Self-organizing Hierarchical Particle Swarm Optimizer with Time-varying Acceleration Coefficients. In Proceedings of IEEE Transactions on Evolutionary Computation, Vol. 8, No. 3, pp. 240–255, 2004. [73] Ross, P. J., Taguchi Techniques for Quality Engineering, second edition McGraw-Hill Professional, 1995. [74] Rousseeuw, P. J. and A.M. Leroy, Robust regression and outlier detection. John Wiley & Sons, New York, 1987. [75] Rousseeuw, P. J., Least Median of Squares Regression. Journal of the American Statistical Association, Vol. 79, No. 388, pp. 871-880, 1984. [76] Salvi, J., An Approach to Coded Structured Light to Obtain Three Dimensional Information, PhD Thesis, Universitat de Girona, 1997. [77] Scharstein, D. and R. Szeliski, A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, Vol. 47, pp. 7-42, 2002. [78] Scharstein, D. and R. Szeliski, Middlebury Stereo Vision Online Benchmark, 2002, Online URL <http://vision.middlebury.edu/stereo/>. [79] Schmid, C. and A. Zisserman, The Geometry and Matching of Curves over Multiple Views. International Journal of Computer Vision, Vol. 40, No. 3, pp.199-233, 2000. [80] Seitz, S. M. and C. R. Dyer, Photorealistic Scene Reconstruction by Voxel Coloring. International Journal of Computer Vision, Vol. 35, pp. 151-173, 1999. [81] Seitz, S.M., B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 519-528, 2006. [82] Seitz, S.M., B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, Middlebury Multi-view Stereo Reconstruction Online Benchmark, 2006, Online URL <http://vision.middlebury.edu/mview/>. [83] Sloane, N. J. A., A Library of Orthogonal Arrays, 2007, http://www2.research.att.com/~njas/oadir/. Accessed 5 November 2012 [84] Snavely, N., Bundler: Structure from Motion for Unordered Image Collections Software, 2010, Online URL < http://phototour.cs.washington.edu/bundler/>. [85] Snavely, N., S. M. Seitz, and R. Szeliski, Microsoft Photo Tourism System, 2006, Online URL < http://phototour.cs.washington.edu/>. [86] Snavely, N., S. M. Seitz, and R. Szeliski, Modeling the World from Internet Photo Collections. International Journal of Computer Vision, Vol. 80, pp. 189-210, 2008. [87] Snavely, N., S. M. Seitz, and R. Szeliski, Photo Tourism: Exploring Photo Collections in 3D. ACM Transactions on Graphics, Vol. 25, pp. 137-154, 2006. [88] Song, P., X. Wu, and M.Y. Wang, Volumetric Stereo and Silhouette Fusion for Image-based Modeling. The Visual Computer Journal, Vol. 26, pp. 1435-1450, 2010. [89] Spetsakis, M. E. and Y. Aloimonos, A Multi-frame Approach to Visual Motion Perception. In Proceedings of International Journal of Computer Vision, pp. 245-255, 1991. [90] Strecha, C., Dense Multi-view Stereo Data Set, 2010, http://cvlab.epfl.ch/~strecha/multiview/denseMVS.html. Accessed 5 November 2012 [91] Strecha, C., W.V. Hansen, L.V. Gool, P. Fua, and U. Thoennessen, On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008. [92] Szeliski, R., Computer Vision: Algorithms and Applications, Springer, Berlin, Germany, First Edition, 2011. [93] Szeliski, R., Image alignment and stitching: A tutorial. Technical Report. MSR-TR-2004-92, Microsoft Research, 2005. [94] Tang, C.-Y., H.-L. Chou, Y.-L. Wu, and Y.-H. Ding, Robust Fundamental Matrix Estimation Using Coplanar Constraints. International Journal of Pattern Recognition and Artificial Intelligence, Vol. 24, No. 4, pp. 783-805, 2008. [95] Tang, C.-Y., Y.-L. Wu, and Y.-H. Lai, Fundamental Matrix Estimation Using Evolutionary Algorithms with Multi-Objective Functions. Journal of Information Science and Engineering, Vol. 24, No. 3, pp. 785-800, 2008. [96] Torr, P.H.S., and A. Zisserman, MLESAC: a New Robust Estimator with Application to Estimating Image Geometry. Computer Vision and Image Understanding, Vol. 78, No. 1, pp. 138-156, 2000. [97] Torr, P.H.S. and A Zisserman, Robust Parametrization and Computation of the Trifocal Tensor. Image and Vision Computing, 15, pp. 591-607, 1997. [98] Torr, P.H.S., A. Zisserman, and S.J. Maybank, Robust Detection of Degenerate Configurations for the Fundamental Matrix. In Proceedings of the IEEE International Conference on Computer Vision, pp. 1037-1042, 1995. [99] Torr, P.H.S., and D.W. Murray, The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix. International Journal of Computer Vision, Vol. 24, No. 3, pp. 271-300, 1997. [100] Torr, P.H.S., Bayesian Model Estimation and Selection for Epipolar Geometry and Generic Manifold Fitting. International Journal of Computer Vision, Vol. 50, No. 1, pp. 35-61, 2002. [101] Tsai, J. T., T. K. Liu, and J. H. Chou, Hybrid Taguchi-genetic Algorithm for Global Numerical Optimization, In Proceedings of IEEE Transactions on Evolutionary Computation, Vol. 8, No. 4, pp. 365-377, 2004. [102] Urquhart, CW, DS Green, ED Borland, 4D Capture Using Passive Stereo Photogrammetry. In IEEE European Conference on Visual Media Production, pp.196 , 2006. [103] Visual Geometry Group, Multi-view and Oxford Colleges Building Reconstruction Data Set (2004), http://www.robots.ox.ac.uk/~vgg/data/data-mview.html. Accessed 5 November 2012. [104] Walpole, R. E., R. H. Myers, S. L. Myers, and K. Ye, Probability & Statistics for Engineers & Scientists, Eighth Edition, Prentice Hall, 2006. [105] Wang, S., L. Chen, A PSO Algorithm Based on Orthogonal Test Design, Fifth International Conference on Natural Computation, pp. 190-194, 2009. [106] Weng, J., N. Ahuja, and T.S. Huang, Closed-form Solution+Maximum Likelihood: a Robust Approach to Motion and Structure Estimation. Computer Vision and Pattern Recognition, pp.381-386, 1988. [107] Weng, M.-F., Y.-Y. Lin, N. C. Tang, and H.-Y. M. Liao, Visual Knowledge Transfer among Multiple Cameras for People Counting with Occlusion Handling. In Proceedings of the ACM International Conference on Multimedia, pp. 1-10, 2012. [108] Wu, Y.-L., C.-Y. Tang, M.-K. Hor, and C.-T. Liu, Automatic Image Interpolation Using Homography. EURASIP Journal on Advances in Signal Processing, 2010. [109] Wu, Y. H. and Z. Y. Hu, A Robust Method to Recognize Critical Configuration for Camera Calibration. Image and Vision Computing, Vol. 24, No. 12, 1313–1318, 2006. [110] Xu, G., Z. Zhang, Epipolar Geometry in Stereo, Motion, and Object Recognition: A Unified Approach, Kluwer Academic Publishers. Norwell, MA, 1996. [111] Yuan, C., G. Medioni, J. Kang, and I. Cohen, Detecting Motion Rregions in the Presence of a Strong Parallax from a Moving Camera by Multiview Geometric Constraints. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 9, pp. 1627–1641, 2007. [112] Yang, J., A. Bouzerdoum, and S. Phung, A particle swarm optimization algorithm based on orthogonal design. In Proceedings of IEEE Congress on Evolutionary Computation, pp. 1-7, 2010. [113] Zhang, Z., Determining the Epipolar Geometry and its Uncertainty: a Review. International Journal of Computer Vision, Vol. 27, No. 2, pp. 161–198, 1998. [114] Zhang, Z. and O. Faugeras, Three-Dimensional Motion Computation and Object Segmentation in a Long Sequence of Stereo Frames. International Journal of Computer Vision, Vol. 7, No. 3, pp. 211-241, 1992. [115] Zhang, Z., Parameter Estimation Techniques: a Tutorial with Application to Conic Fitting. Image and Vision Computing Journal, Vol. 15, No. 1, pp. 59-76, 1997. [116] Zhu, S., and K.-K. Ma, A New Diamond Search Algorithm for Fast Block-Matching Motion Estimation. IEEE Transcations Image Processing, Vol.9, No.2, pp.287-290, 2000.
描述:	博士國立政治大學資訊科學學系 96753501 102
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0096753501
資料類型:	thesis
顯示於類別:	[資訊科學系] 學位論文

文件中的檔案:

檔案	大小	格式	瀏覽次數
350101.pdf	12814Kb	Adobe PDF2	153	檢視/開啟

在政大典藏中所有的資料項目都受到原著作權保護.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回饋