Recovering shape and motion by a dynamic system for low-rank matrix approximation in L 1 norm

Author:

Liu Yiguang,Cao Liping,Liu Chunling,Pu Yifei,Cheng Hong

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Reference36 articles.

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3. Torresani, L., Hertzmann, A., Bregler, C.: Nonrigid structure-from-motion: estimating shape and motion with hierarchical priors. IEEE Trans. Pattern Anal. Mach. Intell. 30, 878–892 (2008)

4. Peng, Y., Ganesh, A., Wright, J., Ma, Y.: RASL: robust alignment by sparse and low-rank decomposition for linearly correlated images. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 700–763 (2010)

5. Wright, J., Ganesh, A., Rao, S., Peng, Y., Ma, Y.: Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization. In: Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C.K.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems, pp. 2080–2088 (2009)

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1. Low-rank and sparse matrix decomposition via the truncated nuclear norm and a sparse regularizer;The Visual Computer;2018-05-24

2. L_1-Norm Low-Rank Matrix Decomposition by Neural Networks and Mollifiers;IEEE Transactions on Neural Networks and Learning Systems;2016-02

3. An Effective Multiview Stereo Method for Uncalibrated Images;Communications in Computer and Information Science;2015

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