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: