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姓名 莊惠閔(Hui-Min Chuang) 查詢紙本館藏 畢業系所 電機工程學系 論文名稱 前瞻3D攝影即時防手震系統
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摘要(中) 在錄製影片的過程中,最難克服的問題就是當以手持攝影機的方式錄製影片時,隨著拍攝時間越長而有越大的可能產生不可避免的手震現象,進而造成錄製的畫面產生相同震動,導致觀賞拍攝影片時會因畫面的不穩定感到不舒適感,所以需要防手震功能將此現象減緩,此外,當以手持3D攝影機的方式錄製3D影片時,錄製的立體影像容易受到手震的影響造成使用者後續觀賞3D影像的舒適度降低,使得手震現象對影片的影響更為突顯。
因此,本篇論文針對防手震之目的,針對一般二維影像之情況與3D立體影像之情況分別探討,並且分別提出針對二維影像防手震系統設計之硬體架構與擴展至3D立體影像之前瞻3D攝影即時防手震演算法。
在單一視角二維影像的防震處理這方面,本篇論文提出適用於高畫質單一視角二維影像即時防震處理之硬體架構,具備高規格影像處理能力,可支援Full HD 1920x1080每秒30張畫面的影像規格,操作頻率為100MHz。
對於3D立體影像防手震的開發,除了考量一般二維影像的手震現象,更要考量畫面中的內容需符合3D立體視訊顯像基本之要求,並避免因為手震的情況降低影片中的3D立體效果,綜合以上考量,本篇論文提出前瞻3D立體影像防手震演算法。除開發演算法外,此篇論文亦將 3D立體影像防手震系統實現於德州儀器(Texas Instruments) Davinci DM6446雙核心嵌入式平台,該系統包含輸出入介面的控制與演算法的執行,介面控制包含了與使用者端之間的影像轉換與將處理完的結果送至顯示端,以達到即時處理之目的。
摘要(英) When recording videos by handheld cameras, the recording method may cause some unwanted vibration in videos. The vibration in video may cause audiences having uncomfortable viewing experience. Audience may want to view videos where no vibration exists. Therefore, the unwanted vibration in videos needs to be removed or reduced. If cameraman films the video using a handheld 3D stereo camera, the vibration in video will cause more problems than traditional 2D monocular video. A stereo camera has two lenses on a single device. The vibrations in video may destroy the 3D relationship between left-view video and right-view video. Thus, audience may not experience deserved stereoscopic effect and feel more uncomfortable due to the destroyed 3D relationships.
Efficient hardware architecture for monocular video stabilization is proposed in this thesis. The performance of the architecture supports the video specification 1920x1080 (Full HD) @ 30 fps, and the system operates at clock rate 100MHz.
In order to solve the problem, different solutions for monocular video stabilization and stereo video stabilization are proposed in this thesis. Efficient hardware architecture is proposed for monocular video stabilization, and advanced 3D video stabilization algorithm is addressed for stereoscopic video.
A 3D stereo video stabilization system should consider not only how to smooth the vibration in video, but also how to maintain the relationships between two cameras. Considering above issues, an advanced 3D stereo video stabilization algorithm is proposed in this thesis.
A porting project is also finished in this thesis. The stereo video stabilization system is porting on Texas Instruments (TI) DaVinci DM6446 evaluation platform. This system contains control of input/output interface and process of the proposed algorithm. This work is finished for achieving the goal of real-time processing.
關鍵字(中) ★ 數位影像防手震
★ 超大型積體電路架構
★ 立體視訊關鍵字(英) ★ Stereoscopic video
★ Digital video stabilization
★ VLSI architecture論文目次 摘要 I
ABSTRACT II
誌謝 IV
TABLE OF CONTENTS VI
LIST OF FIGURES VIII
LIST OF TABLES X
CHAPTER 1 INTRODUCTION 1
1.1 MOTIVATION 2
1.2 SYSTEM OVERVIEW 3
1.3 THESIS ORGANIZATION 4
CHAPTER 2 OVERVIEW OF STEREOSCOPIC VIDEO AND VIDEO STABILIZATION SYSTEM 5
2.1 OVERVIEW OF STEREOSCOPIC VIDEO 6
2.1.1 INTRODUCTION OF STEREOSCOPIC VISION 6
2.1.2 INTRODUCTION OF STEREOSCOPIC VIDEO DISPLAY TECHNIQUES 9
2.1.3 INTRODUCTION OF STEREO MATCHING 12
2.2 OVERVIEW OF VIDEO STABILIZATION 15
2.2.1 INTRODUCTION OF MONOCULAR VIDEO STABILIZATION 15
2.2.2 INTRODUCTION OF STEREO VIDEO STABILIZATION 19
CHAPTER 3 DESIGN OF STEREO VIDEO STABILIZATION 20
3.1 PROPOSED STEREO VIDEO STABILIZATION ALGORITHM 21
3.1.1 CONSIDERATION ISSUES IN DESIGN 22
3.1.2 STEREO VIEW ADJUSTMENT 24
3.1.3 STEREO MATCHING 25
3.1.4 STEREO GLOBAL MOTION ESTIMATION AND STEREO MOTION SMOOTH 27
3.1.5 STEREO MOTION COMPENSATION 28
3.1.6 ANAGLYPH 3D TRANSFORMATION 31
3.2 PLATFORM-BASED DESIGN OF STEREO VIDEO STABILIZATION 32
3.2.1 OVERVIEW OF TI DAVINCI EVALUATION PLATFORM 32
3.2.2 PORTING ON EVALUATION PLATFORM 37
3.3 EXPERIMENTAL RESULT 41
3.3.1 STEREO VIEW ADJUSTMENT RESULTS 41
3.3.2 STEREO MATCHING RESULTS 43
3.3.3 ANAGLYPH 3D TRANSFORMATION RESULTS 44
3.3.4 STEREO VIDEO STABILIZATION RESULTS 47
CHAPTER 4 HARDWARE ARCHITECTURE OF MONOCULAR VIDEO STABILIZATION 52
4.1 OVERVIEW OF PROPOSED ARCHITECTURE 53
4.2 CORNER POINT DETECTION MODULE 54
4.3 LOCAL MOTION ESTIMATION MODULE 56
4.4 GLOBAL MOTION ESTIMATION MODULE 59
4.5 HARDWARE IMPLEMENTATION RESULT 65
CHAPTER 5 CONCLUSION AND FUTURE WORK 68
REFERENCE 69
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[2] D. Scharstein and R. Szeliski, “A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms,” International Journal of Computer Vision (IJCV), 47(1/2/3), pp. 7-42, April 2002.
[3] Andreas Klaus, Mario Sormann and Konrad Karner, “Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure.” International Conference on Pattern Recognition (ICPR), 2006.
[4] Qingxiong Yang, Student member, IEEE, Liang Wang, Student Member, IEEE, Ruigang Yang, Member, IEEE, Henrik Stewenius, Member, IEEE, and David Nister, Member, IEEE.,”Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propogation and Occlusion Handing,” IEEE Transation on Pattern Analysis and Maching Intellignmce (TPAMI), vol. 31, no. 3, March 2009.
[5] Chia-Kai Liang, Chao-Chung Cheng, Yen-Chieh Lai, Liang-Gee Chen and Homer H. Chen, “Hardware-Efficient Belief Propagation,” IEEE Transations on Circuits and Systems for Video Technology (TCSVT), vol. 21, issue 5, pp. 525-537, May 2011.
[6] M. Hariyama, T. Takeuchi and M. Kameyama, “VLSI processor for reliable stereo matching based on adaptive window-size selection,” IEEE International Conference on Robotics and Automation (ICRA), 2001.
[7] Rostam Affendi Hamzah, Rosman Abd Rahim and Zarina Mohd Noh, “Sum of Absolute Differences Algorithm in Correspondence Problem for Stereo Matching in Computer Vision Applization,” IEEE International Conference on Computer Science and Information Technology (ICCSIT), 2010.
[8] Luigi Di Stefano, Massimiliano Marchionni and Stefano Mattoccia, “A Fast Area-based Stereo Matching Algorithm,” Image and Vision Computing 22, pp. 983-1005, 2004.
[9] Martin Humenberger, Christian Zinner, Michael Weber, Wilfried Kubinger and Markus Vincze, “A Fast Stereo Matching Algorithm Suitable For Embedded Real-time System,” Computer Vision and Image Understanding 114, pp. 1180-1202, 2010.
[10] Chih-Lun Fang, “Design of Feature Extraction for Text-Video Processing and Video Stabilization System with Computer Vision-Based Techniques,” Unpublished Doctoral Dissertation, National Central University, Taiwan.
[11] Chun-Wei Liu, “Trajectory-based Stereoscopic Video Stabilization,” Master Thesis, National Taiwan University, Taiwan.
[12] Ken-Yi Lee, Yung-Yu Chuang, Bing-Yu Chen and Ming Ouhyoung, “Video Stabilization using robust feature trajectories,” IEEE International Conference on Computer Vision (ICCV), 2009.
[13] Kai Ki Lee, Kin Hong Wong, Michael Ming Yuen Chang, Ying Kin Yu and Man Kin Leung, “Extended Kalman Filtering Approach to Stereo Video Stabilization,” International Conference on Pattern Recognition (ICPR), 2008.
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[16] Texas Instrument TMS320DM6446 Documentations http://focus.ti.com/docs/prod/folders/print/tms320dm6446.html
[17] Texas Instrument E2E Community http://e2e.ti.com/support/w/forums/ti-e2e-community-forums-site-map.aspx
[18] Minoru 3D Webcam Website http://www.minoru3d.com/
[19] Linux TV http://linuxtv.org/
[20] 3DTV.AT http://www.3dtv.at/
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[23] Eric Dubois, "A Projection Method to Generate Anaglyph Stereo Images," IEEE International Conference on Acoustics Speech Signal Processing (ICASSP ‘01), vol. 3, pp. 1661-1664, May 2001.
[24] Eric Dubois, "Conversion of a Stereo Pair to Anaglyph with the Least-Squares Projection Method," March 2009.
指導教授 蔡宗漢(Tsung-Han Tsai) 審核日期 2011-8-19 推文 facebook plurk twitter funp google live udn HD myshare reddit netvibes friend youpush delicious baidu 網路書籤 Google bookmarks del.icio.us hemidemi myshare