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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳宜良 | |
| dc.contributor.author | Chien-Tsung Huang | en |
| dc.contributor.author | 黃乾宗 | zh_TW |
| dc.date.accessioned | 2021-06-08T01:49:54Z | - |
| dc.date.copyright | 2016-08-02 | |
| dc.date.issued | 2016 | |
| dc.date.submitted | 2016-07-28 | |
| dc.identifier.citation | [1] T. Brox, A. Bruhn, N. Papenberg, and J. Weickert. High accuracy optical flow estimation based
on a theory for warping. In T. Pajdla and J. Matas, editors, Proc. 8th European Conference on Computer Vision, volume 3024 of LNCS, pages 25–36. Springer, May 2004. [2] M. J. Black and P. Anandan. Robust dynamic motion estimation over time. In Proc. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 292–302, Maui, HI, June 1991. IEEE Computer Society Press. [3] M. J. Black and P. Anandan. The robust estimation of multiple motions: parametric and piecewise smooth flow fields. Computer Vision and Image Understanding, 63(1):75–104, Jan. 1996. [4] A. Bruhn and J. Weickert. Towards ultimate motion estimation: Combining highest accuracy with real-time performance. In Proc. 10th International Conference on Computer Vision, pages 749–755. IEEE Computer Society Press, Beijing, China, Oct. 2005. [5] T. Brox, C. Bregler, and J. Malik. Large displacement optical flow. In Proc. International Conference on Computer Vision and Pattern Recognition, 2009. [6] D. Sun, S. Roth, J. P. Lewis, and M. J. Black. Learning optical flow. In Proc. European Conference on Computer Vision, volume 5304 of LNCS, pages 83–87. Springer, 2008. [7] C.L. Zitnick and S.B. Kang, “Stereo for Image-Based Rendering Using Image Over-Segmentation,” Int’l J. Computer Vision, vol. 75, pp. 49-65, Oct. 2007. [8] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Ssstrunk. Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. on PAMI, 34(11), 2012. [9] A. Lucchi, K. Smith, R. Achanta, V. Lepetit, and P. Fua, “A Fully Automated Approach to Segmentation of Irregularly Shaped Cellular Structures in EM Images,” Proc. Int’l Conf. Medical Image Computing and Computer Assisted Intervention, 2010. [10] J. Foley, A. van Dam, S. Feiner, and J. Hughes. Computer Graphics: Principles and Practice, 2nd Edition. Addison- Wesley, 1990. [11] P. Viola and M. Jones. Robust real-time object detection. In IJCV, 2001. [12] Geman, S., Geman, D.: Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images. IEEE TPAMI 6 (1984) 721–741 [13] Tomaso Poggio, Vincent Torre, and Christof Koch. Computational vision and regularization theory. Nature, 317:314–319, 1985. [14] Demetri Terzopoulos. Regularization of inverse visual problems involving discontinuities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(4):413–424, 1986. [15] http://www.vis.uky.edu/~cheung/courses/ee639/Hammersley-Clifford_Theorem.pdf [16] D. Greig, B. Porteous, and A. Seheult. Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society, Series B, 51(2):271–279, 1989. [17] Olga Veksler. EFficient Graph-based Energy Minimization Methods in Computer Vision. PhD thesis, Cornell University, July 1999. [18] A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi. ”TurboPixels: Fast Superpixels Using Geometric Flows. IEEE Trans. on PAMI, 2009. [19] Y. J. Lee, J. Kim, and K. Grauman. Key-segments for video object segmentation. In ICCV, 2011. [20] C. Rother, V. Kolmogorov, and A. Blake. Grabcut: Interactive foreground extraction using iterated graph cuts. In SIGGRAPH, 2004. [21] T.Wang and J. Collomosse. Probabilistic motion diffusion of labeling priors for coherent video segmentation. IEEE Trans. Multimedia, 2012. [22] T. Ma and L. Latecki. Maximum weight cliques with mutex constraints for video object segmentation. In CVPR, pages 670–677, 2012. [23] D. Tsai, M. Flagg, and J. Rehg. Motion coherent tracking with multi-label mrf optimization. In BMVC, page 1, 2010. [24] P. Chockalingam, N. Pradeep, and S. Birchfield. Adaptive fragments-based tracking of non-rigid objects using level sets. In ICCV, pages 1530–1537, 2009. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/19234 | - |
| dc.description.abstract | 在雜亂的環境中，從背景模型穩健地分割前景是困難的一個難題。我們提出能穩健地推定背景和在這樣的環境中檢測感興趣區域的方法。大部分舊有方法是利用大量的疊代運算去計算前景與背景的能量模型，但這樣會很依賴好的初始條件，並耗費大量的運算時間以分析影像。針對這些限制，在此提出了有效率的能量模型計數基於馬可夫隨機場為主要架構。首先建立有效前景預估，再來更精細地標記前景背景，最後得到快於其他方法的前景背景分離。 | zh_TW |
| dc.description.abstract | Robust foreground object segmentation via background modelling is a difficult problem in cluttered environments, where obtaining a clear view of the background to model is almost impossible. We propose a method capable of robustly estimating the background and detecting regions of interest in such environments. Most existing techniques thus adapt an iterative approach for foreground and background appearance modeling. However, these approaches may rely on good initialization and can be easily trapped in local optimal. In addition, they are usually time consuming for analyzing videos. To address these limitations, we propose an efficient appearance modeling technique for automatic primary video object segmentation in the MRF framework. We create an efficient initial foreground estimation. Then we use foreground-background labelling refinement. Finally, we can get the foreground from video faster than other approaches. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T01:49:54Z (GMT). No. of bitstreams: 1 ntu-105-R02221028-1.pdf: 11264505 bytes, checksum: 16e90d2fff6eebc6e0e40f93002ef630 (MD5) Previous issue date: 2016 | en |
| dc.description.tableofcontents | 口試委員會審定書
Acknowledgement . . . . . . . . . . . . . . . . . . .i 中文摘要 . . . . . . . . . . . . . . . . . . . . . . .ii Abstract . . . . . . . . . . . . . . . . . . . . . .iii Table of Contents . . . . . . . . . . . . . . . . . iv List of Figures. . . . . . . . . . . . . . . . . . . .v List of Tables. . . . . . . . . . . . . . . . . . . .vi 1. Introduction . . . . . . . . . . . . . . . . . . . 1 2. Efficient initial foreground estimation . . . . . .3 2.1 Optical flow . . . . . . . . . . . . . . . . 3 2.2 Superpixel . . . . . . . . . . . . . . . . . .7 2.3 Inside-outside maps . . . . . . . . . . . . .13 3. Foreground-background labelling refinement. . . . 15 3.1 Energy minimization in early vision. . . . . 17 3.2 Relationship to Gibbs Fields . . . . . . . .19 3.3 Energy function . . . . . . . . . . . . . . .21 4. Results. . . . . . . . . . . . . . . . . . . . . 27 5. Conclusion . . . . . . . . . . . . . . . . . . . 45 Reference . . . . . . . . . . . . . . . . . . . . . .47 | |
| dc.language.iso | en | |
| dc.title | 基於馬可夫隨機場之影像動態前景偵測與追蹤 | zh_TW |
| dc.title | Dynamic Foreground Detection and Tracking from Video using Markov Random Field | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 104-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 曾正男,黃文良 | |
| dc.subject.keyword | 馬可夫隨機場,光流法,超像素,內外分割圖,能量函數, | zh_TW |
| dc.subject.keyword | Markov random fields,Optical flow,Superpixel,Inside-outside maps,Energy function, | en |
| dc.relation.page | 49 | |
| dc.identifier.doi | 10.6342/NTU201601244 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2016-07-28 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 數學研究所 | zh_TW |
| 顯示於系所單位： | 數學系 | |
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| ntu-105-1.pdf 未授權公開取用 | 11 MB | Adobe PDF |
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