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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳中平 | |
| dc.contributor.author | Tai-Yi Wu | en |
| dc.contributor.author | 吳泰毅 | zh_TW |
| dc.date.accessioned | 2021-06-16T17:23:18Z | - |
| dc.date.available | 2017-08-20 | |
| dc.date.copyright | 2012-08-20 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-08-16 | |
| dc.identifier.citation | [1] Pan, H., C. Yuan, et al. (2011). '3D video disparity scaling for preference and prevention of discomfort.' 786306-786306-786308.
[2] S. Kosov, T. Thormaehlen, H. Seidel, Accurate real-time disparity estimation with variational methods, in: 5th International Symposium on Advances in Visual Computing, Lecture Notes In Computer Science, vol. 5875, Springer-Verlag, 2009, pp. 796–807. [3] T. Tao, J. C. Koo and H. R. Choi. “A fast block matching algorthim for stereo correspondence”, IEEE Conference on Cybernetics and Intelligent Systems, pp. 38-41, 2008 [4] W. Li and E. Salari, 'Successive elimination algorithm for motion estimation', IEEE Trans. Image Processing, vol. 4, pp.105 -107 1995 [5] S. Forstmann, Y. Kanou, J. Ohya, S. Thuering, A. Schmitt, Real-time stereo by using dynamic programming, in: IEEE Conference on Computer Vision and Pattern Recognition Workshop, vol. 3, 2004, pp. 29–36. [6] Cox, I., Hingorani, S., Rao, S., and Maggs, B. 1996. A maximum likelihood stereo algorithm. CVIU: Image Understanding, 63(3):542-567. [7] A. Woods, T. Docherty, and R. Koch, 'Image distortions in stereoscopic video systems', Proc. SPIE Conf. Stereoscopic Displays and Applications , pp.36 -48 1993 [8] D. Kim and K. Sohn, “Depth adjustment for stereoscopic image using visual fatigue prediction and depth-based view synthesis,” in Proc. of 2010 IEEE International Conference on Multimedia and Expo, pp.956-961, Jul.2010 [9] M. T. M. Lambooij and W. A. IJsselsteij, “Visual Discomfort and Visual Fatigue of Stereoscopic Displays: A Review,” J. Imaging Sci. Technol., 53, pp. 1-14, May 2009 [10] M. T. M. Lambooij, W. A. IJsselsteij, and I. Heynderickx,” Visual Discomfort in Stereoscopic Displays: A Review,” SPIE-IS&T, vol. 6490, pp. 1-13, 2007 [11] G. D. Diao, ” 3D Display and Application,” Electronics and Optoelectronics Research Laboratories, Industrial Technology Research Institute, Taiwan, Tech. Rep.,2010. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/63926 | - |
| dc.description.abstract | 現今立體顯示技術已經被廣泛地運用在多媒體應用，在這十年間，已經有許多立體視覺的研究與應用。傳統電影院觀賞立體影像的方法有兩種，分別是利用紅藍眼鏡與偏光眼鏡來區分左右影像，使左右眼個別接收不同的影像。當你的眼睛接收到的存在著視差的影像對，立體的感覺就會被人的大腦所產生。
但有些立體影像在拍攝時並沒有考慮到品質(例如:解析度、控制攝影條件精確度)，所以這種立體影像可能就會讓人感到不舒服。 我們根據觀賞者的對於立體視覺內容的舒適感與偏好建立一個完整的自動化流程調整影像內容。首先我們使用進階的全域搜尋區塊比對演算法與動態規劃演算法估測視差，然後根據人類視覺舒適條件使用深度繪圖法產生新的舒適影像。在深度繪圖法中，我們利用移動拍攝相機的距離調整視差，並且所有立體視覺繪圖的式子可以被近似成一個簡單的式子藉以調整視差。 | zh_TW |
| dc.description.abstract | Stereo display technique is popular for multimedia application now day. Researches and applications on stereo vision have been existed for decades. In conventional stereo theater, one watches movies with “3D Anaglyph Glasses” or “3D Polarizer”. The purpose is to make our eyes accept two different images. When our eyes watch the two images with eyes’ parallax difference, the 3D image will be constructed in our brain automatically.
Sometimes, if the 3D contents represented by conventional 3D video format are filmed without quality controlled (e.g. high definition, precise control of the shooting conditions), so they are uncomfortable possibly. We present a complete flow of automation for tuning stereoscopic 3D content based on viewers’ comfort and preferences. 2D image disparities are computed by exhaustive block matching based estimation algorithm and dynamic programming algorithm. Human visual comfort models are applied to analyze the image disparities and guide the depth tuning (shifting/scaling) in order to generate new stereo views by DIBR with desired and comfortable depth perception. The 3D image warping equations for DIBR are introduced. In our system the shift-camera-separation setup is used, and the 3D image warping equations are simplified to a formula which implies horizontal parallax. Real-life image results are shown to demonstrate the effectiveness of our approach. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T17:23:18Z (GMT). No. of bitstreams: 1 ntu-101-R99945028-1.pdf: 3113098 bytes, checksum: 0b263c2bd4c536db63dbd7ba55d85177 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES x Chapter 1 Introduction 1 Chapter 2 Overview of Binocular Stereoscopies on 3DTV Display 2 2.1 Depth Perception 3 2.1.1 Monocular Depth Cues 3 2.1.1.1 Linear Perspective 3 2.1.1.2 Interposition 4 2.1.1.3 Texture Gradient 5 2.1.1.4 Color 5 2.1.1.5 Accommodation 6 2.1.1.6 Motion Parallax 7 2.1.1.7 Shadow 7 2.1.1.8 Familiar and Relative Size 8 2.1.2 Binocular Depth Cues 10 2.1.2.1 Convergence 10 2.1.2.2 Binocular Disparity (Stereopsis) 11 2.1.2.3 Horizontal Parallax 11 2.2 Technologies of 3D Display 14 2.3 Causative Factors of Visual Fatigued 17 2.3.1 Excessive binocular disparity 17 2.3.2 Accommodation and convergence mismatch 18 2.3.3 Stereoscopic distortion 20 2.4 Percival’s Zone of Comfort 21 Chapter 3 Overview of Adjustment of Video Stream 22 3.1 Pre-Processing 23 3.1.1 Conversion of YCbCr 24 3.1.2 Calibration of Alignment and Angle 25 3.1.2.1 Sobel Operator 26 3.1.2.2 Sweep to Find the Shift and Angle 27 3.1.3 Correction of Color Difference 27 3.2 Extraction of Disparity 28 3.2.1 Estimation of Disparity 30 3.2.1.1 Successive Elimination Algorithm for Disparity Estimation 30 3.2.1.2 Block Based Dynamic Programming 33 3.2.2 Histogram from Disparity and Correction of Disparity Distribution 34 3.3 Depth Image Based Rendering 35 3.3.1 Rendering with Comfort Zone 35 3.3.1.1 Geometry of stereoscopic video systems 35 3.3.1.2 Simplification of formula 42 3.3.1.3 Image Rendering 43 3.3.2 Hole filling 49 Chapter 4 Experimental Result 50 4.1 Pre-Processing 50 4.1.1 Calibration of Angle and Alignment 50 4.1.2 Color Correction 53 4.2 Extraction of Disparity 54 4.2.1 Estimation of Disparity 54 4.2.1.1 Successive Elimination Algorithm 54 4.2.1.2 Block Based Dynamic Programming 55 4.2.1.3 Correction of Disparity 55 4.3 Depth Image Based Rendering 57 4.3.1 Rendering 57 4.3.2 Hole Filling 58 REFERENCE 59 | |
| dc.language.iso | en | |
| dc.subject | 三維 | zh_TW |
| dc.subject | 立體 | zh_TW |
| dc.subject | 深度資訊 | zh_TW |
| dc.subject | 視差估計 | zh_TW |
| dc.subject | 雙眼視差 | zh_TW |
| dc.subject | 深度圖繪圖法 | zh_TW |
| dc.subject | depth image base rendering (DIBR) | en |
| dc.subject | 3D | en |
| dc.subject | binocular parallax | en |
| dc.subject | depth information | en |
| dc.subject | disparity estimation | en |
| dc.subject | Stereo | en |
| dc.title | 基於人因分析之增強人眼觀賞三維影像舒適度演算法 | zh_TW |
| dc.title | Visual Comfort Enhancement Algorithms for 3D Movie – according to Human Factor Analysis | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 傅楸善,洪士灝 | |
| dc.subject.keyword | 立體,三維,雙眼視差,深度資訊,視差估計,深度圖繪圖法, | zh_TW |
| dc.subject.keyword | Stereo,3D,binocular parallax,depth information,disparity estimation,depth image base rendering (DIBR), | en |
| dc.relation.page | 60 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-08-16 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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| ntu-101-1.pdf 未授權公開取用 | 3.04 MB | Adobe PDF |
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