会议信息
相关文章见: [1] Hongwei Yong, Deyu Meng, Wangmeng Zuo, Lei Zhang. Robust Online Matrix Factorization for Dynamic Background Subtraction, TPAMI, 2017.[arxiv version][code] [2] Yang Chen, Xiangyong Cao, Qian Zhao, Deyu Meng, Zongben Xu. Denoising Hyperspectral Image with Non-i.i.d. Noise Structure. arXiv:1702.00098, 2017. [3]Xiangyong Cao, Qian Zhao, Deyu Meng, Yang Chen, Zongben Xu. Robust Low-rank Matrix Factorization under General Mixture Noise Distributions, IEEE Transactions on Image Processing, 2016. [4] Xi’ai Chen, Zhi Han, Yao Wang, Qian Zhao, Deyu Meng and Yandong Tang. Robust tensor factorization with unknown noise. CVPR, 2016 [5] Xiangyong Cao, Yang Chen, Qian Zhao, Deyu Meng, Yao Wang, Dong Wang, Zongben Xu. Low-rank Matrix Factorization under General Mixture Noise Distributions. ICCV (oral), 2015.[supplementary material] [Matlab code] [arxiv version] [6] Qian Zhao, Deyu Meng, Zongben Xu, Wangmeng Zuo, Lei Zhang. Robust principal component analysis with complex noise, Supplementary Material, ICML, 2014. [7] Deyu Meng, Fernando De la Torre. Robust Matrix Factorization with Unknown Noise. ICCV, 2013.Matlab code. [8] Qian Zhao, Deyu Meng, Zongben Xu, Wangmeng Zuo, Yan Yan. L1-Norm Low-Rank Matrix Factorization by Variational Bayesian Method. IEEE Transactions on Neural Networks and Learning Systems. 2015. [9] Chenqiang Gao, Deyu Meng, Yi Yang, Yongtao Wang, Xiaofang Gao, Alexander G. Hauptmann. Infrared Patch-image Model for Small Target Detection in A Single Image. IEEE Transactions on Image Processing. 22(12): 4996-5009, 2013. Matlab Code [10] Deyu Meng, Zongben Xu, Lei Zhang, Ji Zhao. A cyclic weighted median method for L1 low-rank matrix factorization with missing entries. AAAI 2013. [11] Deyu Meng, Qian Zhao, Zongben Xu. Improve robustness of sparse PCA by L1-norm maximization. Pattern Recognition. 2012, 45(1) 487-497.
误差建模的基本原理可通过以下科普文章简要了解: [11] 孟德宇,误差建模原理,人工智能学会通讯,2016.
Related Demos for OMoGMF (Please be patient to wait a few minutes. Some of the gif files need a bit time to be fully depicted.)
Related Demos on our submission "Robust Online Dynamic Background Subtraction":
1. Experimental results on Li dataset: (1) Airport sequence:
(2) Bootstrap sequence:
(3) Curtain sequence:
(4) Escalator sequence:
(5) Fountain sequence:
(6) Shoppingmall sequence:
(7) Lobby sequence:
(8) Campus sequence:
(9) Watersurface sequence:
t-OMoGMF experients 2. Experimental results on transformed Li dataset: (1) Airport sequence:
(2) Curtain sequence:
(3) Escalator sequence:
(4) Fountain sequence:
(5) Shoppingmall sequence:
(6) Lobby sequence:
(7) Campus sequence:
(8) Watersurface sequence:
3. Experimental results on real-world dataset: (1) Sidewalk sequence:
(2) Boulevard sequence:
(3) Badminton sequence:
(4) Traffic sequence:
4. Performance comparison on real-world dataset: (1) Sidewalk sequence:
(2) Boulevard sequence:
(3) Badminton sequence:
(4) Traffic sequence:
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