臺灣博碩士論文加值系統

本論文主要採用立體像素法(voxel-based method)來重建三維物體模型，系統可分為四個部分。第一部份為相機校正(camera calibration)，目的是取得相機的內、外部參數；第二部分為影像分割(image segmentation)，目的是將物體從背景中分離出來；第三部分為三維模型重建，目標是得到三維物體表面點的座標與顏色資訊，而在此步驟中會使用到兩種方法，分別是立體像素可見度(voxel visibility)及色彩一致性(color consistency)，作法是在三維世界座標中，先建立一個立方體包含有 個立體像素(voxel)，將每一個立體像素透過相機參數投影在相機的影像平面上，再利用立體像素可見度及色彩一致性方法重建三維物體，其中色彩一致性是本論文重點；第四部分為顯示介面，主要利用VC++程式及OpenGL函式庫將所建立的三維模型顯示出來。
目前常用的色彩一致性方法有單一臨界值法(single threshold method)、直方圖法(histogram method) 及適應臨界值法(adaptive threshold method)三種方法，在此提出一種新的色彩一致性方法，主要利用密度基礎群聚法 (density-based clustering method)來實現色彩一致性，並與其它三種色彩一致性方法做比較。由實驗結果得知，密度基礎群聚法能有效刪除不需要的立體像素且能精確得到立體像素的顏色，使得重建模型更接近實際的三維物體。

A voxel-based approach for 3D object reconstruction is used in this thesis, and there are four steps in the process of a voxel-based 3D reconstruction system. In the first step, the camera is calibrated, and the purpose of camera calibration is to acquire the intrinsic and extrinsic parameters of the camera. Second, image segmentation is executed to extract object from background. Third, a 3D model is built, and the coordinates and colors information of a large amount of surface points of the object are determined. The third step includes two sub-steps that are voxel visibility and color consistency, and color consistency is the main issue of this thesis. Finally, a reconstructed 3D object is displayed by computer language VC++ with OpenGL libraries in the fourth step.
So far, generally speaking, there are three different methods for implementing color consistency, and these three methods are single threshold method, histogram method and adaptive threshold method. A new color consistency method by using the density-based clustering method is proposed in the thesis, and the proposed method is compared with the other three color consistency methods. According to the experimental results, the proposed method can eliminate the unnecessary voxels and determine the true colors of voxels very well.

中文摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 x

第一章 緒論 1
1.1 相關研究 1
1.2 研究動機 2
1.3 論文架構 4

第二章 相關知識及理論 5
2.1 相機校正原理 6
2.2 影像分割技術 11
2.2.1 色彩空間轉換 11
2.2.2 臨界值法 13
2.2.3 型態學運算 14
2.3 立體像素色彩法 16
2.4 DBSCAN演算法 19

第三章 以立體像素為基礎之三維模型重建系統 21
3.1 系統概述 22
3.2 相機校正 23
3.3 影像分割 26
3.4 立體像素可見度 29
3.5 色彩一致性 31
3.5.1 單一臨界值法 31
3.5.2 適應臨界值法 31
3.5.3 直方圖法 33
3.5.4 密度基礎群聚法 34
3.6 顯示介面 37

第四章 實驗結果與分析 38
4.1 實驗機制 38
4.1.1 峰值訊噪比 39
4.1.2 投影誤差 39
4.2 實驗結果 40
4.3 實驗分析 69
第五章 結論 72
參考文獻 73

[1] M. Levoy, “The Digital Michelangelo project: 3D scanning of large status,” in Proc. SIGGRAPH 2000, pp. 134-141, 2000.
[2] Z. Chen, S. Y. Ho, and D. C. Tseng, “Polyhedral face reconstruction and modeling from a single image with structured light,” IEEE Trans. Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 864-872, 1993.
[3] G. Hu and G. Stockman, “3-D Surface Solution Using Structured Light and Constraint Propagation,” IEEE Trans. on Pattern Analysis Machine Intelligence, vol. 11 , no. 4, pp. 390-402, 1989.
[4] Y. F. Wang, A. Mitiche, and J. K. Aggarwal, “Computation of surface orientation and structure of objects using grid coding,” IEEE Trans. on Pattern Analysis Machine Intelligence, vol. 9 , no.1, pp. 129-137, 1987.
[5] T. S. Huang and A. N. Netravali, “Motion and structure from feature correspondences: A review,” Processing of IEEE, vol. 82, no. 2, pp. 251-268, 1994.
[6] B. Garcia and P. Brunet, “3D Reconstruction with projective Octrees and Epipolar Geometry,” Computer Vision 1998. Sixth International Conference, pp. 1067-1072, 1998.
[7] S. M. Seitz and C. R. Dyer, “Photorealistic scene reconstruction by voxel coloring,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1067-1073, 1997.
[8] R. Y. Tsai, “A versatile camera calibration technique for high accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses,” IEEE Trans. Robotic and Automation, vol. 3, no. 4, pp. 323-444, 1987.
[9] R. C. Gonzalez and R. E. Woods, Digital Image Processing. Upper Saddle River, NJ: Prentice Hall, 2002.
[10] K. N. Kutulakos and S. M. Seitz, “A theory of shape by spacing carving,” Int. J. of Computer Vision, vol. 38, no. 3, pp. 199-218, 2000.
[11] W. B. Culbertson and T. Malzbender, “Generalized voxel coloring,” in Proc. ICCV Workshop, Vision Algorithms Theory and Practice, Springer Verlag Lecture Notes in Computer Science, pp. 100-115, 2001.
[12] J. Yin and J. R. Cooperstock, “A new photo consistency test for voxel coloring,”
Computer and Robot Vision, 2005. Proceedings. The 2nd Canadian Conference on Volume , Issue , pp. 566-570, 2005.
[13] H. P. Kriegel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” In Proc. 1996 Int. Conf. Knowledge Discovery and Data Mining (KDD’96), pp. 226-231, Portland, OR, 1996.
[14] J. Y. Bouguet, [Online]. Available: http://www.vision.caltech.edu/bouguetj/
[15] G. G. Slabaugh, W. B. Culbertson, T. Malzbender, M. R. Stevens, and R. W. Schafer, “Method for volumetric reconstruction of visual Scenes,” Int. J. of Computer Vision, vol. 57, no. 3, pp. 179-199, 2004.
[16] M. R. Stevens, B. Culbertson, and T. Malzbender, “A histogram-based color consistency test for voxel coloring”, in Proc. IEEE Conf. Pattern Recognition, vol. 4, pp. 118-121, 2002.
[17] G. G. Slabaugh, T. Malzbender, W. B. Culbertson, and R. W. Schafer, “Improved voxel coloring via volumetric optimization,” Center for Signal and Image Processing Technical Report TR3, Georgia Institute of Technology, 2000.
[18] C. Zhao and W. Xiao, “ A Parallelizing and Improving on Voxel Coloring Technology for 3D Reconstruction Based on Images,” in Proc. IEEE Conf. Pattern Recognition, vol. 4, pp. 118-121, 2002.