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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 陳少傑(Sao-Jie Chen) | |
| dc.contributor.author | Hao Lee | en |
| dc.contributor.author | 李皓 | zh_TW |
| dc.date.accessioned | 2021-06-16T16:03:26Z | - |
| dc.date.available | 2018-07-08 | |
| dc.date.copyright | 2013-07-08 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-07-02 | |
| dc.identifier.citation | Bibliography
[1] The Nielsen Company, U.S. consumers show high interest in 3DTV, but cite some concerns, September 2010. http://www.nielsen.com/us/en.html [2] R. Zone, 2007. Stereoscopic Cinema and the Origins of 3-D Film, 1838-1952. Kentucky: The University Press of Kentucky. [3] S. Yano, S. Ide, T. Mitsuhashi and H. Thwaites, “A Study of Visual Fatigue and Visual Comfort for 3D HDTV/HDTV Images,” Displays, Volume 23, Issue 4, Pages 191-201, September 2002. [4] T. Shibata, J. Kim, D. M. Hoffman and M. S. Banks, “The Zone of Comfort: Predicting Visual Discomfort with Stereo Displays,” Journal of Vision, Volume 11, Number 8, Article 11, July 21, 2011. [5] D. M. Hoffman, A. R. Girshick, K. Akeley and M. S. Banks, “Vergence–Accommodation Conflicts Hinder Visual Performance and Cause Visual Fatigue,” Journal of Vision, Volume 8, Number 3, Article 33, March 28, 2008. [6] S. Yanoa, M. Emotoa, and T. Mitsuhashia, “Two factors in visual fatigue caused by stereoscopic HDTV images,” Displays, Volume 25, Issue 4, Pages 141-150, November 2004. [7] M. Lambooij and W. IJsselsteijn, “Visual Discomfort and Visual Fatigue of Stereoscopic Displays: A Review,” Journal of Imaging Science and Technology, Volume 53, Number 3, Pages 30201-1-30201-14(14), May2009. [8] N. A. Dodgson, “Analysis of the Viewing Zone of Multiview Autostereoscopic displays,” Proc. SPIE Stereoscopic Displays and Virtual Reality Systems IX, 254, Volume 4660, Pages 254–265, May 24, 2002. [9] I. Sexton and P. Surman, “Stereoscopic and Autostereoscopic Display Systems,” Proc. IEEE Signal Processing Magazine, Volume 16, Issue 3, Pages 85-99, May 1999. [10] J. G. Ferwerda, 1982. The World of 3-D: A Practical Guide to Stereo Photography. USA, Canada: Reel 3-D Enterprises. [11] A. Woods, T. Docherty, and R. Koch, “Image Distortions in Stereoscopic Video Systems,” Proc. SPIE Stereoscopic Displays and Applications, Pages 36-48, February 1993. [12] N. A. Dodgson, J. R. Moore, and S. R. Lang, “Multi-view Autostereoscopic 3D Display,” International Broadcasting Convention, Pages 497-502, September 1999. [13] E. Dubois, “A Projection Method to Generate Anaglyph Stereo Images,” Proc. ICASSP Acoustics, Speech, and Signal Processing, Volume 3, Pages 1661-1664, May 2001. [14] D. Scharstein and R. Szeliski, Middlebury Stereo Vision Page. http://vision.middlebury.edu/stereo/ [15] C. P. Lin, “High Speed FPGA-based Hardware Implementation of 3D Video Optimization for Human Visual Comfort Enhancement,” M.S. thesis, Nation Taiwan University, Taipei, ON, Taiwan, 2012. [16] T. Y. Wu, “Automation of Disparity Adjustment According to Human Factor for Depth-Image-Based Rendering,” M.S. thesis, Nation Taiwan University, Taipei, ON, Taiwan, 2012. [17] R. Gupta and S. Y. Cho, “Real-time Stereo Matching using Adaptive Binary Window,” Proc. 3D Data Processing, Visualization and Transmission, 2010. [18] Z. Yanga, F. L. Chung and W. Shitonga, “Robust Fuzzy Clustering-based Image Segmentation,” Applied Soft Computing, Volume 9, Issue 1, Pages 80-84, January 2009. [19] T. N. Pappas, “An Adaptive Clustering Algorithm for Image Segmentation,” Trans. IEEE Signal Processing, Volume 40, Issue 4, Pages 901-914, April 1992. [20] G. B. Coleman and H. C. Andrews, “Image Segmentation by Clustering,” Proc. IEEE, Volume 67, Issue 5, Pages 773-785, May 1979. [21] A. Y. Yang, J. Wright, Y. Ma and S. S. Sastry, “Unsupervised Segmentation of Natural Images via Lossy Data Compression,” Computer Vision and Image Understanding Archive, Volume 110, Issue 2, Pages 212-225, May 2008. [22] M. Nakajima, T. Watanabe, H. Koga, “Compression-based semantic-sensitive image segmentation: PRDC-SSIS,” Geoscience and Remote Sensing Symposium IEEE International, Pages 4303-4306, July 2012. [23] S. Kamdi and R. K. Krishna, “Image Segmentation and Region Growing Algorithm,” International Journal of Computer Technology and Electronics Engineering, Volume 2, Issue 1. [24] T. Chen and Z. Q. Shen, “An Adaptive Image Segmentation Method using Region Growing,” Computer Engineering and Technology, Volume 7, Pages V7-78 - V7-80, April 2010. [25] M. M. S. J. Preetha, L. P. Suresh and M. J. Bosco, “Image Segmentation using Seeded Region Growing,” Computing, Electronics and Electrical Technologies, Pages 576-583, March 2012. [26] S. Y. Chen, W. C. Lin and C. T. Chen, “Split-and-merge Image Segmentation based on Localized Feature Analysis and Statistical Tests” CVGIP: Graphical Models and Image Processing, Volume 53, Issue 5, Pages 457-475, September 1991. [27] K. Laws, “Integrated Split/Merge Image Segmentation,” Technical Note 441, Artificial Intelligence Center, SRI International, 1988. [28] D. Scharstein and R. Szeliski, “A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms,” International Journal of Computer Vision archive, Volume 47, Issue 1-3, Pages 7-42, April-June 2002. [29] D. DeCarlo and D. Metaxas, “The Integration of Optical Flow and Deformable Models with Applications to Human Face Shape and Motion Estimation,” Proc. Computer Vision and Pattern Recognition IEEE Computer Society Conference, Pages 231-238, June 1996. [30] J. R. Bergen, P. Anandan, K. J. Hanna and R. Hingorani, “Hierarchical Model-based Motion Estimation,” Computer Science, Volume 588, Pages 237-252, 1992. [31] X. Mei, X. Sun, M. Zhou and S. Jiao, “On Building an Accurate Stereo Matching System on Graphics Hardware,” Computer Vision Workshops (ICCV Workshops) IEEE International Conference, Pages 467-474, November 2011. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62502 | - |
| dc.description.abstract | 在此篇論文中，我們提出了一個可將雙視角立體數位內容轉為多視角立體數位內容的方法。此方法是以人因實驗之結果作為基礎，對輸入之立體數位內容做深度方面的最佳化，以保證觀測者觀看時不感到視覺疲勞、頭暈等負面感受。
隨著立體電影再次在面板業間引起熱潮，兩個關鍵因素仍然抑制著立體電視蓬勃發展。第一個因素為人在觀測立體數位內容時，常感到視覺疲勞或頭暈等症狀，而另一個因素為觀測立體內容時必須配戴專用的立體眼鏡的不方便性。第一個問題已由此篇論文所敘述之方法解決，而隨著面板製造業的廠商正努力於解決第二個問題，此篇論文同時提出了可由雙視角立體數位內容轉換出多視角立體數位內容的方法，防止未來裸眼立體電視大量生產時相應的多視角立體數位內容供不應求。此套方法所涉及的相關技術包含視差計算、影像分割、深度調整法、深度傳播以及影像濾波等。 此方法各個步驟中包含了多個新提出的演算法，其中包含了高精確度的視差計算方法、加速視差計算的方法、改善經深度調整後之影片連續性的方法、避免調整深度時產生嚴重變形的方法，以及最終目的－改善觀看立體內容時視覺疲勞或不適的問題。為了檢測這些新方法的效果，我們設計了幾項實驗。針對視差計算的方法，我們與其他已公佈之方法比較精確度與速度。至於可相容於其他系統的幾種加速或改善方法，我們以自己建立的系統測試使用了這些方法的前後結果。實驗結果顯示這些新提出之方法確實有效並可改善整個立體數位內容處理系統。 | zh_TW |
| dc.description.abstract | In this thesis, a complete method that generates visual fatigue free multi-view 3D contents from two-view 3D contents is proposed.
As a new trend of 3D arises in the display industry, two critical problems still stand in the way, visual fatigue and the inconveniency of wearing 3D glasses. The first problem is resolved by adjusting the disparity range of a given stereo content. Techniques involved in this process include stereo matching, color segmentation, clustering, depth image based rendering (DIBR), depth propagation, etc. Display manufacturers are still working on products that may overcome the second problem. In the meantime, we focus on developing two-view to multi-view conversion tools so that an abundance of multi-view contents can await the appearance of the first multi-view auto-stereoscopic display on the market. Techniques used include depth image based rendering (DIBR), disparity map pre-processing and image filtering. Experiments were designed to evaluate the effectiveness of several newly proposed algorithms. The purposes of these algorithms include generating high-quality disparity maps, accelerating the process of generating disparity maps, increasing the fluency of 3D video contents, preventing distortion after view rendering and the overall effect of reducing visual fatigue. The resulting disparity maps of our stereo matching tool were compared with other published algorithms, while other proposed techniques that can be adopted by other systems were tested by comparing the before and after results of our own system. The experiment results strongly support the benefits of these algorithms. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T16:03:26Z (GMT). No. of bitstreams: 1 ntu-102-R99943145-1.pdf: 2947271 bytes, checksum: e628cfff73d68768418c8c9283f0f256 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | CONTENTS
誌謝 i 摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES vii LIST OF TABLES xi Chapter 1 Introduction 1 1.1 An Introduction to the 3D Industry 1 1.2 Visual Comfort 2 1.3 Auto-stereoscopy 3 1.4 Motivation 4 1.5 Thesis Organization 4 Chapter 2 Background 5 2.1 3D Camera Systems 6 2.1.1 Parallel 3D Cameras 6 2.1.2 Converged 3D Cameras 8 2.2 3D Display Systems 9 2.2.1 Stereoscopic Displays 10 2.2.2 Auto-stereoscopic Displays 13 2.3 Depth Perception 15 2.4 Visual Fatigue 17 Chapter 3 Proposed Techniques 19 3.1 Adaptive Binary Window Block Matching 20 3.1.1 Adaptive Binary Window 20 3.1.2 Initial Disparity Estimation 22 3.1.3 Disparity Map Refinement 25 3.2 Region-based Disparity Optimization 29 3.2.1 Color Segmentation 29 3.2.2 Region Adjacency Graph Construction 31 3.2.3 Cost Minimization 32 3.3 Viewpoint Optimization 34 3.4 Depth Image Based Rendering 37 3.5 Acceleration and Quality Enhancement 42 3.5.1 Disparity Range Estimation 43 3.5.2 Depth Propagation 44 3.5.3 Temporal Noise Reduction 48 3.6 Modified Flow for Video Contents 48 Chapter 4 Experiment Results 50 4.1 Time Reduction with Disparity Range Estimation 50 4.2 Key Frame Detection Accuracy 52 4.3 Speed/Fluency Improvement with Depth Propagation 53 4.4 Stereo Matching Performance Comparison 56 4.5 Effectiveness of Visual Comfort Optimization 58 Chapter 5 Conclusion and Future Work 63 Bibliography 64 | |
| dc.language.iso | en | |
| dc.subject | 深度傳播 | zh_TW |
| dc.subject | 裸眼立體 | zh_TW |
| dc.subject | 多視角立體數位內容 | zh_TW |
| dc.subject | 立體 | zh_TW |
| dc.subject | 三維 | zh_TW |
| dc.subject | 視差計算 | zh_TW |
| dc.subject | 深度調整法 | zh_TW |
| dc.subject | 影像分割 | zh_TW |
| dc.subject | 視覺疲勞 | zh_TW |
| dc.subject | 人因實驗 | zh_TW |
| dc.subject | 3D | en |
| dc.subject | multi-view | en |
| dc.subject | visual fatigue | en |
| dc.subject | depth image based rendering (DIBR) | en |
| dc.subject | color segmentation | en |
| dc.subject | stereo matching | en |
| dc.subject | motion vector | en |
| dc.subject | Stereoscopy | en |
| dc.subject | disparity | en |
| dc.title | 以人因分析為基礎之立體影像觀看舒適度最佳化及
雙視角轉多視角方法 | zh_TW |
| dc.title | A Visual Comfort Optimizing Two-view to Multi-view Stereo Content Conversion Method based on Human Factor Analysis | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 陳中平(Chung-Ping Chen) | |
| dc.contributor.oralexamcommittee | 傅楸善(Chiou-Shann Fuh),洪士灝(Shih-Hao Hung) | |
| dc.subject.keyword | 立體,三維,視差計算,深度調整法,影像分割,視覺疲勞,人因實驗,深度傳播,裸眼立體,多視角立體數位內容, | zh_TW |
| dc.subject.keyword | Stereoscopy,3D,visual fatigue,multi-view,disparity,stereo matching,color segmentation,depth image based rendering (DIBR),motion vector, | en |
| dc.relation.page | 67 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-07-02 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
| Appears in Collections: | 電子工程學研究所 | |
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| File | Size | Format | |
|---|---|---|---|
| ntu-102-1.pdf Restricted Access | 2.88 MB | Adobe PDF |
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