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Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

Neural networks, 2019-09, Vol.117, p.8-66

2019

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Background-Foreground Modeling Based on Spatiotemporal Sparse Subspace Clustering

IEEE transactions on image processing, 2017-12, Vol.26 (12), p.5840-5854

2017

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Spatiotemporal Low-Rank Modeling for Complex Scene Background Initialization

IEEE transactions on circuits and systems for video technology, 2018-06, Vol.28 (6), p.1315-1329

2018

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Pixel-associated autoencoder for hyperspectral anomaly detection

International journal of applied earth observation and geoinformation, 2024-05, Vol.129, p.103816, Article 103816

2024

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Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset

Computer science review, 2017-02, Vol.23, p.1-71

2017

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Moving Object Detection in Complex Scene Using Spatiotemporal Structured-Sparse RPCA

IEEE transactions on image processing, 2019-02, Vol.28 (2), p.1007-1022

2019

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Learning Soft Mask Based Feature Fusion with Channel and Spatial Attention for Robust Visual Object Tracking

Sensors (Basel, Switzerland), 2020-07, Vol.20 (14), p.4021

2020

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Unsupervised deep context prediction for background estimation and foreground segmentation

Machine vision and applications, 2019-04, Vol.30 (3), p.375-395

2019

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Development of STEM-Based AI Education Program for Sustainable Improvement of Elementary Learners

Sustainability, 2022-11, Vol.14 (22), p.15178

2022

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Face De-Identification Using Face Caricature

IEEE access, 2024, Vol.12, p.19344-19354

2024

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Improving Object Tracking by Added Noise and Channel Attention

Sensors (Basel, Switzerland), 2020-07, Vol.20 (13), p.3780

2020

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Efficient Visual Tracking With Stacked Channel-Spatial Attention Learning

IEEE access, 2020, Vol.8, p.100857-100869

2020

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High-Quality Face Caricature via Style Translation

IEEE access, 2023, Vol.11, p.138882-138896

2023

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Motion-Aware Graph Regularized RPCA for background modeling of complex scenes

2016 23rd International Conference on Pattern Recognition (ICPR), 2016, p.120-125

2016

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Frontiers of Computer Vision: 26th International Workshop, IW-FCV 2020, Ibusuki, Kagoshima, Japan, February 20-22, 2020, Revised Selected Papers

1st Edition 2020, 2020

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In‐Vehicle AR‐HUD System to Provide Driving‐Safety Information

ETRI Journal, 2013, 35(6), , pp.1038-1047

2013

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Designing Gamified Application: An Effective Integration of Augmented Reality to Support Learning

IEEE access, 2022, Vol.10, p.121385-121394

2022

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Background/Foreground Separation: Guided Attention based Adversarial Modeling (GAAM) versus Robust Subspace Learning Methods

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021, p.181-188

2021

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Two-Phase Calibration for a Mirror Metaphor Augmented Reality System

Proceedings of the IEEE, 2014-02, Vol.102 (2), p.196-203

2014

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Online Stochastic Tensor Decomposition for Background Subtraction in Multispectral Video Sequences

2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 2015, p.946-953

2015

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A Scene-Specific Object Detection System Utilizing the Advantages of Fixed-Location Cameras

Journal of information and communication convergence engineering, 2023, Vol.21 (4), p.329-336

2023

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Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

Computers, materials & continua, 2021, Vol.68 (3), p.4087-4108

2021

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Background Subtraction via Superpixel-Based Online Matrix Decomposition with Structured Foreground Constraints

2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 2015, p.930-938

2015

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Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends

ACM computing surveys, 2019-05, Vol.52 (2), p.1-44, Article 43

2019

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Unsupervised moving object segmentation using background subtraction and optimal adversarial noise sample search

Pattern recognition, 2022-09, Vol.129, p.108719, Article 108719

2022

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Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

Background-Foreground Modeling Based on Spatiotemporal Sparse Subspace Clustering

Spatiotemporal Low-Rank Modeling for Complex Scene Background Initialization

Pixel-associated autoencoder for hyperspectral anomaly detection

Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset

Moving Object Detection in Complex Scene Using Spatiotemporal Structured-Sparse RPCA

Learning Soft Mask Based Feature Fusion with Channel and Spatial Attention for Robust Visual Object Tracking

Unsupervised deep context prediction for background estimation and foreground segmentation

Development of STEM-Based AI Education Program for Sustainable Improvement of Elementary Learners

Face De-Identification Using Face Caricature

Improving Object Tracking by Added Noise and Channel Attention

Efficient Visual Tracking With Stacked Channel-Spatial Attention Learning

High-Quality Face Caricature via Style Translation

Motion-Aware Graph Regularized RPCA for background modeling of complex scenes

Frontiers of Computer Vision: 26th International Workshop, IW-FCV 2020, Ibusuki, Kagoshima, Japan, February 20-22, 2020, Revised Selected Papers

In‐Vehicle AR‐HUD System to Provide Driving‐Safety Information

Designing Gamified Application: An Effective Integration of Augmented Reality to Support Learning

Background/Foreground Separation: Guided Attention based Adversarial Modeling (GAAM) versus Robust Subspace Learning Methods

Two-Phase Calibration for a Mirror Metaphor Augmented Reality System

Online Stochastic Tensor Decomposition for Background Subtraction in Multispectral Video Sequences

A Scene-Specific Object Detection System Utilizing the Advantages of Fixed-Location Cameras

Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

Background Subtraction via Superpixel-Based Online Matrix Decomposition with Structured Foreground Constraints

Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends

Unsupervised moving object segmentation using background subtraction and optimal adversarial noise sample search

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