Low Rank and Sparse Matrix Decomposition as Stroke Segmentation Prior: Useful or Not? A Random Forest-Based Evaluation Study

2017 ◽  
pp. 161-166 ◽  
Author(s):  
René Werner ◽  
Daniel Schetelig ◽  
Thilo Sothmann ◽  
Eike Mücke ◽  
Matthias Wilms ◽  
...  
Keyword(s):  
Random Forest ◽  
Sparse Matrix ◽  
Matrix Decomposition ◽  
Evaluation Study ◽  
Low Rank

A blind spectrum sensing based on low-rank and sparse matrix decomposition

2018 ◽  
Vol 15 (8) ◽  
pp. 118-125
Author(s):  
Junsheng Mu ◽  
Xiaojun Jing ◽  
Hai Huang ◽  
Ning Gao
Keyword(s):  
Spectrum Sensing ◽  
Sparse Matrix ◽  
Matrix Decomposition ◽  
Low Rank

scholarly journals Speech Denoising via Low-Rank and Sparse Matrix Decomposition

ETRI Journal ◽  
2014 ◽  
Vol 36 (1) ◽  
pp. 167-170 ◽  
Author(s):  
Jianjun Huang ◽  
Xiongwei Zhang ◽  
Yafei Zhang ◽  
Xia Zou ◽  
Li Zeng
Keyword(s):  
Sparse Matrix ◽  
Matrix Decomposition ◽  
Low Rank ◽  
Speech Denoising

scholarly journals Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components

2014 ◽  
Vol 73 (3) ◽  
pp. 1125-1136 ◽  
Author(s):  
Ricardo Otazo ◽  
Emmanuel Candès ◽  
Daniel K. Sodickson
Keyword(s):  
Sparse Matrix ◽  
Matrix Decomposition ◽  
Dynamic Mri ◽  
Low Rank ◽  
Dynamic Components

Low-rank and sparse matrix decomposition via the truncated nuclear norm and a sparse regularizer

2018 ◽  
Vol 35 (11) ◽  
pp. 1549-1566 ◽  
Author(s):  
Zhichao Xue ◽  
Jing Dong ◽  
Yuxin Zhao ◽  
Chang Liu ◽  
Ryad Chellali
Keyword(s):  
Sparse Matrix ◽  
Matrix Decomposition ◽  
Nuclear Norm ◽  
Low Rank ◽  
Truncated Nuclear Norm

scholarly journals Flying Small Target Detection for Anti-UAV Based on a Gaussian Mixture Model in a Compressive Sensing Domain

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2168 ◽  
Author(s):  
Chuanyun Wang ◽  
Tian Wang ◽  
Ershen Wang ◽  
Enyan Sun ◽  
Zhen Luo
Keyword(s):  
Compressive Sensing ◽  
Target Detection ◽  
Sparse Matrix ◽  
Infrared Image ◽  
Matrix Decomposition ◽  
Gaussian Mixture ◽  
Visual Surveillance ◽  
Low Rank ◽  
Small Target ◽  
Small Target Detection

Addressing the problems of visual surveillance for anti-UAV, a new flying small target detection method is proposed based on Gaussian mixture background modeling in a compressive sensing domain and low-rank and sparse matrix decomposition of local image. First of all, images captured by stationary visual sensors are broken into patches and the candidate patches which perhaps contain targets are identified by using a Gaussian mixture background model in a compressive sensing domain. Subsequently, the candidate patches within a finite time period are separated into background images and target images by low-rank and sparse matrix decomposition. Finally, flying small target detection is achieved over separated target images by threshold segmentation. The experiment results using visible and infrared image sequences of flying UAV demonstrate that the proposed methods have effective detection performance and outperform the baseline methods in precision and recall evaluation.

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