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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 莊永裕 | |
dc.contributor.author | Yu-Hsiang Huang | en |
dc.contributor.author | 黃昱翔 | zh_TW |
dc.date.accessioned | 2021-06-07T17:57:13Z | - |
dc.date.copyright | 2012-08-16 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-08-13 | |
dc.identifier.citation | [1] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool. Speeded-up robust features (SURF). CVIU, 110(3):346–359, June 2008.
[2] C.-H. Chang, C.-K. Liang, and Y.-Y. Chuang. Content-aware display adaptation and interactive editing for stereoscopic images. IEEE Transactions on Multimedia, 13(4):589–601, August 2011. [3] M. Farre, O. Wang, M. Lang, N. Stefanoski, A. Hornung, and A. Smolic. Automatic content creation for multiview autostereoscopic displays using image domain warping. In Multimedia and Expo (ICME), 2011 IEEE International Conference on, pages 1–6, July 2011. [4] C. Fehn. A 3D-TV approach using depth-image-based rendering. Proceedings of 3rd IASTED Conference on Visualization, Imaging, and Image Processing, 3:482–487, 2003. [5] C. Fehn. Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV. Stereoscopic Displays and Virtual Reality Systems XI. Proceedings of the SPIE, 5291:93–104, 2004. [6] Y.-H. Huang, T.-K. Huang, Y.-H. Huang, W.-C. Chen, and Y.-Y. Chuang. Warping-based novel view synthesis from a binocular image for autostereoscopic displays. In Multimedia and Expo (ICME), 2012 IEEE International Conference on, pages 302–307, July 2012. [7] ISO/IEC JTC1/SC29/WG11. View synthesis reference software, May 2009. version 3.0. [8] ISO/IEC JTC1/SC29/WG11. Depth estimation reference software, July 2010. version 5.0. [9] M. Lang, A. Hornung, O. Wang, S. Poulakos, A. Smolic, and M. Gross. Nonlinear disparity mapping for stereoscopic 3D. ACM Transactions on Graphics, 29(4):75:1–75:10, 2010. [10] F. Liu, M. Gleicher, H. Jin, and A. Agarwala. Content-preserving warps for 3D video stabilization. ACM Transactions on Graphics, 28(3):44:1–44:9, 2009. [11] D. Lowe. Object recognition from local scale-invariant features. In Proceedings of ICCV, volume 2, pages 1150–1157, 1999. [12] B. D. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. In Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2, IJCAI’81, pages 674–679, San Francisco, CA, USA, 1981. Morgan Kaufmann Publishers Inc. [13] E. Rosten and T. Drummond. Fusing points and lines for high performance tracking. In IEEE International Conference on Computer Vision, volume 2, pages 1508–1511, October 2005. [14] E. Rosten and T. Drummond. Machine learning for high-speed corner detection. In European Conference on Computer Vision, volume 1, pages 430–443, May 2006. [15] B. Smith, L. Zhang, and H. Jin. Stereo matching with nonparametric smoothness priors in feature space. In Proceedings of CVPR, pages 485–492, June 2009. [16] A. Smolic, P. Kauff, S. Knorr, A. Hornung, M. Kunter, M. M‥uandller, and M. Lang. Three-dimensional video postproduction and processing. Proceedings of the IEEE, 99(4):607–625, April 2011. [17] C. Tomasi and T. Kanade. Detection and tracking of point features. Technical report, International Journal of Computer Vision, 1991. [18] T. Tuytelaars. Dense interest points. In Proceedings of CVPR, pages 2281–2288, June 2010. [19] R. von Gioi, J. Jakubowicz, J.-M. Morel, and G. Randall. LSD: A fast line segment detector with a false detection control. IEEE TAPMI, 32(4):722–732, April 2010. [20] H.Wang, M. Sun, and R. Yang. Space-time light field rendering. IEEE Transactions on Visualization and Computer Graphics, 13(4):697–710, July 2007. [21] Y.-S. Wang, C.-L. Tai, O. Sorkine, and T.-Y. Lee. Optimized scale-and-stretch for image resizing. ACM Transactions on Graphics, 27(5):118:1–118:8, 2008. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/15985 | - |
dc.description.abstract | 本論文提出一個以雙眼立體影像的基於型變的新視角影像生成方法。裸眼立體顯示器需要多視角影像(包含圖片及影片)作為輸入,但現今大多立體相機只能擷取兩個視角(左右眼)的影像。近年來較為流行的新視角影像方法,如深度影像繪圖法,大多很依賴準確的深度圖以取得好結果,然而準確的深度圖目前仍是難以取得的。我們所提出的方法不需要深度圖,並且也不需要使用者的介入。對於生成多視角圖片的部分,首先我們偵測密集且可靠的特徵點。接著我們利用特徵點的對應關係去引導圖片的型變以生成新視角影像,並且在型變的過程中保持立體性質及維持內容結構。基於相同的架構,我們修改了針對圖片的方法使之能處理影片,加強了對於時間軸上的對應關係並且加快生成的速度。相較於深度影像繪圖法,我們提出的方法可以以高效率產生高品質的多視角影像,而且可以免除惱人的參數設定。此方法可以直接將立體相機所拍攝雙眼影像轉換成能在裸眼立體顯示器上撥放的多視角立體影像。 | zh_TW |
dc.description.abstract | This thesis presents a warping-based novel view synthesis framework for both binocular stereoscopic images and videos. Autostereoscopic displays require multiple views while most stereoscopic cameras can only capture two. Popular novel view synthesis methods, such as depth image based rendering (DIBR), often heavily rely on accurate depth maps, which are still difficult to obtain. The proposed framework requires neither depth maps nor user intervention. To synthesize multi-view images, it first extracts dense and reliable features. Next, feature correspondences guide image warping to synthesize novel views while simultaneously maintaining stereoscopic properties and preserving image structures. Based on the same framework, a modified method for binocular videos are proposed to better maintaining temporal coherence and accelerating the processing speed. Compared to DIBR, the proposed framework produces higher-quality multi-view images and videos more efficiently without tedious parameter tuning. The method can be used to convert stereoscopic images and videos taken by binocular cameras into multi-view images and videos ready to be displayed on autostereoscopic displays. | en |
dc.description.provenance | Made available in DSpace on 2021-06-07T17:57:13Z (GMT). No. of bitstreams: 1 ntu-101-R99922020-1.pdf: 6095707 bytes, checksum: 1aa2f03f679ab71f7380dacedfe452d8 (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | 致謝i
中文摘要ii Abstract iii 1 Introduction 1 2 Multi-View Image Synthesis 5 2.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1 Dense Interest Points . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.2 Content-Preserving Warps . . . . . . . . . . . . . . . . . . . . . 6 2.1.3 Line Bending . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.1 Semi-Dense Stereo Correspondence . . . . . . . . . . . . . . . . 8 2.2.2 Virtual View Generation . . . . . . . . . . . . . . . . . . . . . . 9 2.2.3 Modified Content-Preserving Warps . . . . . . . . . . . . . . . . 10 2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3 Multi-View Video Synthesis 20 3.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2.1 Semi-Dense Stereo Correspondences . . . . . . . . . . . . . . . 22 3.2.2 Virtual View Generation . . . . . . . . . . . . . . . . . . . . . . 24 3.2.3 Triangular Mesh Warps . . . . . . . . . . . . . . . . . . . . . . . 24 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4 Conclusion and Future Work 27 Bibliography 29 | |
dc.language.iso | en | |
dc.title | 基於型變的雙眼影像新視角生成快速演算法 | zh_TW |
dc.title | Fast Warping-Based Novel View Synthesis from Binocular Image/Video for Autostereoscopic Displays | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳維超,鄭文皇,胡敏君 | |
dc.subject.keyword | 新視角影像生成,裸眼立體視覺,形變, | zh_TW |
dc.subject.keyword | novel view synthesis,autostereoscopy,warping, | en |
dc.relation.page | 32 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2012-08-14 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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