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Deep Neural Network Concepts for Background Subtraction: A Systematic
  Review and Comparative Evaluation

Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation

13 November 2018
T. Bouwmans
S. Javed
M. Sultana
Soon Ki Jung
ArXivPDFHTML

Papers citing "Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation"

13 / 13 papers shown
Title
Learning Temporal Distribution and Spatial Correlation Towards Universal
  Moving Object Segmentation
Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object Segmentation
Guanfang Dong
Chenqiu Zhao
Xichen Pan
Anup Basu
VOS
16
3
0
19 Apr 2023
Optical flow-based branch segmentation for complex orchard environments
Optical flow-based branch segmentation for complex orchard environments
A. You
C. Grimm
J. Davidson
17
9
0
26 Feb 2022
An Empirical Review of Deep Learning Frameworks for Change Detection:
  Model Design, Experimental Frameworks, Challenges and Research Needs
An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs
Murari Mandal
Santosh Kumar Vipparthi
17
82
0
04 May 2021
Universal Background Subtraction based on Arithmetic Distribution Neural
  Network
Universal Background Subtraction based on Arithmetic Distribution Neural Network
Chenqiu Zhao
Kang-Ting Hu
Anup Basu
24
21
0
16 Apr 2021
BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic
  Supervised Background Subtraction
BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction
M. Tezcan
Prakash Ishwar
Janusz Konrad
13
76
0
23 Jan 2021
Advances in Electron Microscopy with Deep Learning
Advances in Electron Microscopy with Deep Learning
Jeffrey M. Ede
19
2
0
04 Jan 2021
"What's This?" -- Learning to Segment Unknown Objects from Manipulation
  Sequences
"What's This?" -- Learning to Segment Unknown Objects from Manipulation Sequences
W. Boerdijk
M. Sundermeyer
M. Durner
Rudolph Triebel
6
7
0
06 Nov 2020
Review: Deep Learning in Electron Microscopy
Review: Deep Learning in Electron Microscopy
Jeffrey M. Ede
15
79
0
17 Sep 2020
Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised
  Anomaly Detection
Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised Anomaly Detection
A. Akhriev
Jakub Mareˇcek
UQCV
22
4
0
09 Dec 2019
Background Subtraction in Real Applications: Challenges, Current Models
  and Future Directions
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
T. Bouwmans
B. G. García
19
268
0
11 Jan 2019
Tensor Robust Principal Component Analysis with A New Tensor Nuclear
  Norm
Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm
Canyi Lu
Jiashi Feng
Yudong Chen
W. Liu
Zhouchen Lin
Shuicheng Yan
49
731
0
10 Apr 2018
DehazeNet: An End-to-End System for Single Image Haze Removal
DehazeNet: An End-to-End System for Single Image Haze Removal
Bolun Cai
Xiangmin Xu
K. Jia
Chunmei Qing
Dacheng Tao
117
2,394
0
28 Jan 2016
MatConvNet - Convolutional Neural Networks for MATLAB
MatConvNet - Convolutional Neural Networks for MATLAB
Andrea Vedaldi
Karel Lenc
183
2,947
0
15 Dec 2014
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