cvpr cvpr2013 cvpr2013-453 cvpr2013-453-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Xiaojie Guo, Xiaochun Cao, Xiaowu Chen, Yi Ma
Abstract: Given an area of interest in a video sequence, one may want to manipulate or edit the area, e.g. remove occlusions from or replace with an advertisement on it. Such a task involves three main challenges including temporal consistency, spatial pose, and visual realism. The proposed method effectively seeks an optimal solution to simultaneously deal with temporal alignment, pose rectification, as well as precise recovery of the occlusion. To make our method applicable to long video sequences, we propose a batch alignment method for automatically aligning and rectifying a small number of initial frames, and then show how to align the remaining frames incrementally to the aligned base images. From the error residual of the robust alignment process, we automatically construct a trimap of the region for each frame, which is used as the input to alpha matting methods to extract the occluding foreground. Experimental results on both simulated and real data demonstrate the accurate and robust performance of our method.
[1] E. Cand e`s, X. Li, Y. Ma, and J. Wright. Robust principal component analysis? Journal of the ACM, 58(3): 1–37, 2011.
[2] Q. Chen, D. Li, and C. Tang. KNN matting. In Proc. of CVPR, pages 869–876, 2012.
[3] M. Cox, S. Lucey, S. Sridharan, and J. Cohn. Least squares congealing for unsupervised alignment of images. In Proc. of CVPR, pages 1–8, 2008.
[4] M. Cox, S. Lucey, S. Sridharan, and J. Cohn. Least-squares congealing for large numbers of images. In Proc. of ICCV, pages 1–8, 2009.
[5] M. Fischler and R. Bolles. Random sample consensus: A paradigm for model tting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381–395, 1981. 222222888977 the laptop’s monitor background changing. Bottom: Outdoor scene with repairing and advertising. More details can be found in the text.
[6] X. Hou and L. Zhang. Color conceptualization. In Proc. of ACM Multimedia, pages 265–268, 2007.
[7] G. Huang, V. Jain, and E. Learned-Miller. Unsupervisedjoint alignment of complex images. In Proc. of ICCV, pages 1–8, 2007.
[8] E. Learned-Miller. Data driven image models through continuous joint alignment. IEEE TPAMI, 28(2):236–250, 2006.
[9] A. Levin, D. Lischinski, and Y. Weiss. A closed-form solution to natural image matting. IEEE TPAMI, 30(2): 1–15, 2008.
[10] Z. Lin, M. Chen, L. Wu, and Y. Ma. The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. Technical Report UILU-ENG-09-2215, UIUC Technical Report, 2009.
[11] A. Nigeau, M. Bertalmio, V. caselles, and G. Sapiro. A comprehensive framework for image inpainting. IEEE Trans. on Image Processing, 19(10):2634–2645, 2010.
[12] Y. Peng, A. Ganesh, J. Wright, W. Xu, and Y. Ma. RASL: Robust alignment by sparse and low-rank decomposition for linearly correlated images. IEEE TPAMI, 34(1 1):2233–2246, 2012.
[13] P. P ´erez, M. Gangnet, and A. Blake. Poisson image editing. In ACM SIGGRAPH, pages 313–318, 2003.
[14] Y. Pritch, E. Kav-Venaki, and S. Peleg. Shift-map image
[15]
[16]
[17]
[18]
[19] editing. In Proc. of ICCV, pages 151–158, 2009. J. Sun, J. Jia, C. Tang, and H. Shum. Poisson matting. In ACM SIGGRAPH, pages 315–321, 2004. A. Vedaldi, G. Guidi, and S. Soatto. Joint alignment up to (lossy) transforamtions. In Proc. of CVPR, pages 1–8, 2008. A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, and Y. Ma. Toward a practical face recognition system: Robust alignment and illumination by sparse representation. IEEE TPAMI, 34(2):373–386, 2012. A. Yang, A. Ganesh, Z. Zhou, S. Sastry, and Y. Ma. A review of fast l1-minimization algorithms for robust face recognition. CoRR abs/1007.3753, 2010. Z. Zhang, A. Ganesh, X. Liang, and Y. Ma. TILT: Transform-invariant low-rank textures. IJCV, 99(1): 1–24, 2012. 222222889088