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 …
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 …
Crack detection in ultrahigh-performance concrete using robust principal component analysis and characteristic evaluation in the frequency domain
J Cao, H He, Y Zhang, W Zhao… - Structural Health …, 2024 - journals.sagepub.com
Studying the crack propagation of ultrahigh-performance concrete (UHPC) helps us
understand its mechanical mechanism and assess its structural performance. A novel …
understand its mechanical mechanism and assess its structural performance. A novel …
A wind power forecasting method based on optimized decomposition prediction and error correction
J Li, S Zhang, Z Yang - Electric Power Systems Research, 2022 - Elsevier
To reduce the effect of nonlinearity and volatility in the wind power time sequence, a two-
stage short-term wind power forecasting method based on optimized decomposition …
stage short-term wind power forecasting method based on optimized decomposition …
Rapid robust principal component analysis: CUR accelerated inexact low rank estimation
Robust principal component analysis (RPCA) is a widely used tool for dimension reduction.
In this work, we propose a novel non-convex algorithm, coined Iterated Robust CUR …
In this work, we propose a novel non-convex algorithm, coined Iterated Robust CUR …
Robust low-rank tensor ring completion
Low-rank tensor completion recovers missing entries based on different tensor
decompositions. Due to its outstanding performance in exploiting some higher-order data …
decompositions. Due to its outstanding performance in exploiting some higher-order data …
Efficient Low-Rank Matrix Factorization Based on ℓ1,ε-Norm for Online Background Subtraction
Background subtraction refers to extracting the foreground from an observed video, and is
the fundamental problem of various applications. There are two kinds of popular methods to …
the fundamental problem of various applications. There are two kinds of popular methods to …
Deep learning approach for human action recognition using a time saliency map based on motion features considering camera movement and shot in video image …
A Alavigharahbagh, V Hajihashemi, JJM Machado… - Information, 2023 - mdpi.com
In this article, a hierarchical method for action recognition based on temporal and spatial
features is proposed. In current HAR methods, camera movement, sensor movement …
features is proposed. In current HAR methods, camera movement, sensor movement …
Robust tensor decomposition based background/foreground separation in noisy videos and its applications in additive manufacturing
Background/foreground separation is one of the most fundamental tasks in computer vision,
especially for video data. Robust PCA (RPCA) and its tensor extension, namely, Robust …
especially for video data. Robust PCA (RPCA) and its tensor extension, namely, Robust …
A general destriping framework for remote sensing images using flatness constraint
K Naganuma, S Ono - IEEE Transactions on Geoscience and …, 2022 - ieeexplore.ieee.org
Removing stripe noise, ie, destriping, from remote sensing images is an essential task in
terms of visual quality and subsequent processing. Most existing destriping methods are …
terms of visual quality and subsequent processing. Most existing destriping methods are …