Deep neural network concepts for background subtraction: A systematic review and comparative evaluation
Conventional neural networks have been demonstrated to be a powerful framework for
background subtraction in video acquired by static cameras. Indeed, the well-known Self …
background subtraction in video acquired by static cameras. Indeed, the well-known Self …
Background subtraction in real applications: Challenges, current models and future directions
Computer vision applications based on videos often require the detection of moving objects
in their first step. Background subtraction is then applied in order to separate the background …
in their first step. Background subtraction is then applied in order to separate the background …
Robust subspace learning: Robust PCA, robust subspace tracking, and robust subspace recovery
Principal component analysis (PCA) is one of the most widely used dimension reduction
techniques. A related easier problem is termed subspace learning or subspace estimation …
techniques. A related easier problem is termed subspace learning or subspace estimation …
On the applications of robust PCA in image and video processing
Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse
matrices offers a powerful framework for a large variety of applications such as image …
matrices offers a powerful framework for a large variety of applications such as image …
An empirical review of deep learning frameworks for change detection: Model design, experimental frameworks, challenges and research needs
M Mandal, SK Vipparthi - IEEE Transactions on Intelligent …, 2021 - ieeexplore.ieee.org
Visual change detection, aiming at segmentation of video frames into foreground and
background regions, is one of the elementary tasks in computer vision and video analytics …
background regions, is one of the elementary tasks in computer vision and video analytics …
Graph moving object segmentation
JH Giraldo, S Javed… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Moving Object Segmentation (MOS) is a fundamental task in computer vision. Due to
undesirable variations in the background scene, MOS becomes very challenging for static …
undesirable variations in the background scene, MOS becomes very challenging for static …
Moving objects detection with a moving camera: A comprehensive review
MN Chapel, T Bouwmans - Computer science review, 2020 - Elsevier
During about 30 years, a lot of research teams have worked on the big challenge of
detection of moving objects in various challenging environments. First applications concern …
detection of moving objects in various challenging environments. First applications concern …
Improved robust tensor principal component analysis via low-rank core matrix
Robust principal component analysis (RPCA) has been widely used for many data analysis
problems in matrix data. Robust tensor principal component analysis (RTPCA) aims to …
problems in matrix data. Robust tensor principal component analysis (RTPCA) aims to …
Moving object detection in complex scene using spatiotemporal structured-sparse RPCA
Moving object detection is a fundamental step in various computer vision applications.
Robust principal component analysis (RPCA)-based methods have often been employed for …
Robust principal component analysis (RPCA)-based methods have often been employed for …
Multiplex cellular communities in multi-gigapixel colorectal cancer histology images for tissue phenotyping
In computational pathology, automated tissue phenotyping in cancer histology images is a
fundamental tool for profiling tumor microenvironments. Current tissue phenotyping methods …
fundamental tool for profiling tumor microenvironments. Current tissue phenotyping methods …