隨著3D播放科技的進步，人們可以不用帶3D的眼鏡，即可利用3D播放器來感受到3D的效果。雖然，我們有上千萬2D影片，然而，現階段3D的內容物還不夠豐富。為了配合3D播放科技之發展，2D轉3D的影像技術變得越來越重要。過去已有論文研究多視角影像估計景深圖之方法，但是對於這些上千萬2D影片，2D影像轉3D影像最需要的方法是直接從原始的2D影像去產生景深圖。
本篇論文中，我們提供了一個用於一般戶外單張背景影像的全自動深度圖產生方法。我們的自動深度圖產生系統中，我們先設計一些影像內容分析器。這些分析器偵測包含了一些我們所設定的材料，如天空、地面、以及樹的切割器來分割這些想要的元素，接下來我們會根據不同的元素的空間概念給定不同的深度值，實驗結果顯示，我們所建議的系統可以有效的偵測需要的元素，並對於戶外背景影像可以產生合理的深度圖。

With 3D display technology improvement, people now can perceive the 3D effect by 3D display without wearing 3D glasses. We have a huge amount of 2D video contents; however, due to 3D contents are not sufficient enough, the 2D to 3D conversion technique becomes more and more important to promote 3D displays in the near future. In the literatures, several researches proposed depth map estimation methods if multi-view images are available. However, for those thousands 2D videos, the most demanding method is to estimate the depth map from original 2D images.
In this thesis, we propose an automatic depth map generation method from general single-view outdoor background images. In the proposed automatic depth map generation system, we first develop some image landscape extractions which can detect some outdoor landscapes, such as sky, ground line, and tree, and then segment the desired objects. After segmentation, we assign the depth value to the extracted objects based on their spatial characteristics. Experimental results show that the proposed method can effectively extracted the desired landscape objects and generates their reasonable depth maps for outdoor images.

[1] K. C. Liu, Q. Wu, and C. F. Wu, “Example-Based Depth Generation Form Single Image For 3D Content,” IEEE Transactions on Broadcasting, vol. 49, no. 1, pp.333-336, Mar. 2008.
[2] N. V. Boulgouris and M. G. Strintzis, “A Family of Wavelet-Based Stereo Image Coders,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 12, no. 10, pp.898-903, Oct. 2002.
[3] H. Inoue, T. Tachikawa, and M. Inaba. “Robot Vision System with a Correlation Chip for Real-Time Tracking, Optical Flow and Depth Map Generation,” in Proc. IEEE Int’l Conf. on Robotics and Automation, pp. 1621–1626, May 1992.
[4] M. R. Boutell, J. Luo and C. M. Brown., “Scene Parsing Using Region-Based Generative Models,” IEEE Transactions on Multimedia, Vol. 9, NO. 1, pp.136-146, Jan. 2007.
[5] A. Singhal, J. Luo, and W. Zhu, “Probabilistic spatial context models for scene content understanding,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Madison, WI, pp.235-241, Jun. 2003.
[6] J.R. Smith and C.-S. Li, “Image Classification and Querying Using Composite Region Templates,” Comput. Vis. Image Understand., vol. 75, no.1/2, pp.165–174, 1999.
[7] L. Lucchese and S. K. Mitra, “A New Method for Color Image Equalization,”in Proc. IEEE Conf. Image processing, vol.1, pp.133–136 Oct. 2001
[8] W. Ali, F. Georgsson and T. Hellstrom “Visual Tree Detection for Autonomous Navigation in Forest Environment,” IEEE Intelligent Vehicles Symposium Eindhoven, pp.560–565, Jun. 2008.
[9] A. Gunay, and V. V. Nabiyev, “Automatic Age Classification with LBP,” IEEE Computer and Information Sciences, 2008. ISCIS '08. 23rd International Symposium pp.1–4, Oct. 2008.
[10] T. Ojala, M. Pietika inen and T. Maenpaa, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, No. 7, pp.971-987, Jul. 2002.
[11] T. Maenpaa, “The Local Binary Pattern Approach to Texture Analysis-Extensions and Applications,” Infotech Oulu and Department of Electrical and Information Engineering, University of Oulu, 2003.
[12] Y. Takahashi and K. Hirata “Segmented-Region Based Approach Using Object Components Database to Detect Sky Region,” IEEE International Conference on Image Processing, 2733-2736, Oct. 2006.
[13] J. Luo, S. P. Etz “A Physical Model-Based Approach to Detecting Sky in Photographic Images” IEEE international transaction on Image Processing, Vol. 11, pp.201 – 212, Mar. 2002.
[14] G. woods “Digital Image Processing” 2/e
[15] E. Rotem, K. Wolowelsky, D. Pelz, “Automatic Video to Stereoscopic Video Conversion” in Proc. Of SPIE-IS&T Electronic on Imaging, SPIE Vol. 5664, pp.198-206, 2005
[16] K. Hosotani, S. Maeda, O. Tohyama, H. Ito,“Fiberscope-type Environmental Monitoring Devices with Binocular Parallax Accommodation Mechanism for Stereoscopic Observation” IEEE. Tenth Annual International Workshop on Micro Electro Mechanical Systems, pp.400-405, Jan. 1997.