Article
Engineering, Electrical & Electronic
Lucas P. Ramos, Dimas I. Alves, Leonardo T. Duarte, Mats I. Pettersson, Renato Machado
Summary: Robust principal component analysis (RPCA) and tensor RPCA (TRPCA) techniques are valuable for ground scene estimation (GSE) in synthetic aperture radar (SAR) imagery, improving the performance of change detection methods.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Environmental Sciences
Dantong Zhu, Zhenhao Zhong, Minghao Zhang, Suqin Wu, Kefei Zhang, Zhen Li, Qingfeng Hu, Xianlin Liu, Junguo Liu
Summary: This study proposes an improved interpolation-based RDPCA method, which takes the PWV derived from ERA5 as an additional aid, to interpolate missing data in precipitable water vapor derived from global navigation satellite systems. The performance of the IRDPCA is compared with RDPCA and DINEOF using simulation experiments, and the results demonstrate the superior performance of the IRDPCA in both the heterogeneous and homogeneous cases. Furthermore, these methods are also applied to the interpolation of the real GNSS-PWV, and the interpolated GNSS-PWV using the IRDPCA is not impacted by the systematic discrepancies in the ERA-PWV and agrees well with the original data.
Article
Environmental Sciences
Wenjie Shen, Yunzhen Jia, Yanping Wang, Yun Lin, Yang Li, Zechao Bai, Wen Jiang
Summary: This paper proposes a new change detection algorithm for spaceborne SAR time-series data based on SAR-SIFT-Logarithm Background Subtraction, which effectively detects the overall change information and reduces processing time compared to traditional pairwise comparison methods.
Article
Environmental Sciences
Damir Borkovic, Zoran Kovac, Ines Krajcar Bronic
Summary: Principal component analysis, Fourier analysis, and wavelet analysis were used to analyze the correlation and seasonal variations among air temperature, precipitation, and stable isotope data. The results showed significant relationships and seasonal patterns.
Article
Mathematics
Azizur Rahman, Depeng Jiang
Summary: This research uses statistical methods based on functional time series analysis to improve mortality rate prediction for the Canadian population. It explores the impact of age differences on mortality series and suggests wider application of functional principal component analysis in public health and age-related policy studies.
Article
Computer Science, Artificial Intelligence
Mahmood Amintoosi, Farzam Farbiz
Summary: This paper discusses the common technique of using dominant eigenvectors for background modeling and proposes an alternative solution by utilizing the weakest eigenvectors for better results.
JOURNAL OF MATHEMATICAL IMAGING AND VISION
(2022)
Article
Computer Science, Artificial Intelligence
Zina Li, Yao Wang, Qian Zhao, Shijun Zhang, Deyu Meng
Summary: Background subtraction of videos is a fundamental research topic in computer vision. However, current methods based on matrix modeling have limitations. To address this issue, we propose a tensor-based online compressive video reconstruction and background subtraction method that can better adapt to complex video scenes.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Review
Mathematics, Interdisciplinary Applications
Xiaofeng Dong, Qingju Fan, Dan Li
Summary: This study investigates the main components of air pollutants by using detrending moving-average cross-correlation analysis (DMCA) and principal component analysis (PCA). The advantages of this method are illustrated through comparative numerical analysis with traditional PCA. The results show that DMCA-based PCA provides more reliable principal components in the small and medium scale range, and is relatively immune to additive trend and non-stationarity. In addition, the study examines the utility of DMCA-based PCA in natural complex systems using seasonal air pollutant data collected in Beijing. The findings reveal that PM2.5, PM10, and CO are the most significant factors affecting air quality in Beijing, with O3 as a secondary pollutant across the four seasons. The stability of the principal component contributions is highest in winter and second highest in autumn. These results, which can be explained physically, demonstrate the usefulness of DMCA-based PCA in addressing non-stationary signals.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Geochemistry & Geophysics
Kunpu Ji, Yunzhong Shen, Qiujie Chen, Tengfei Feng
Summary: Traditional principal component analysis (PCA) assumes homogeneity in global navigation satellite system (GNSS) time series, requiring restoration of missing data prior to analysis. To directly process incomplete and heterogeneous GNSS position time series, this study introduces extended PCA (EPCA) and weighted EPCA approaches to estimate missing values based on best low-rank approximation in the spatiotemporal domain. The proposed methods successfully extract common mode errors (CMEs) from real GNSS position time series of 24 stations in North China. Comparative analysis against modified PCA (MPCA) demonstrates that EPCA outperforms MPCA in extracting CMEs, reducing noise, and improving site velocity estimates. Weighted EPCA and weighted MPCA also outperform their unweighted counterparts, with the former showing superior performance. Additionally, EPCA exhibits computational efficiency by requiring estimation of fewer unknowns compared to MPCA.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Interdisciplinary Applications
Raanju R. Sundararajan
Summary: Dimension reduction techniques for multivariate time series transform the observed series into lower-dimensional multivariate subseries using a spectral domain method, allowing for decomposition and reconstruction of the series.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2021)
Article
Mathematics, Applied
Lynne Billard, Ahlame Douzal-Chouakria, S. Yaser Samadi
Summary: Time-series data are widely studied in machine learning and data analysis for classification and clustering. However, most existing methods do not fully utilize the time-dependency information of the data. This study proposes a new method that extends principal component analysis to cross-autocorrelation matrices at different time lags to capture the main dynamic structure of multivariate time series. Experimental results on simulated data and a sign language dataset demonstrate the effectiveness and advantages of the proposed method.
Article
Biochemistry & Molecular Biology
Ying Zou
Summary: This paper proposes a more comprehensive mathematical model to correctly judge the category of ancient glass products whose chemical composition changes due to weathering. The author systematically analyzes the surface weathering of glass relics and its correlation with three properties, establishing a multivariable time-series model to predict the chemical-composition content before weathering. Through one-way analysis of variance for subclassification and principal component analysis, the reasonable prediction of the chemical-composition content and classification is provided, which can be used in the protection of cultural relics, historical research, and other fields.
Article
Physics, Fluids & Plasmas
Tanja Zerenner, Marc Goodfellow, Peter Ashwin
Summary: Harmonic cross-correlation decomposition (HCD) is introduced as a tool to detect and visualize features in the frequency structure of multivariate time series. HCD decomposes time series into spatiotemporal harmonic modes representing dominant oscillatory patterns, and can visually relate phase spectra to phase relations in the data.
Article
Computer Science, Hardware & Architecture
Xiaoji Wan, Hailin Li, Liping Zhang, Yenchun Jim Wu
Summary: The paper proposes a method based on Principal Component Analysis (PCA) called Piecewise Representation based on PCA (PPCA) to effectively reduce the dimensionality in multivariate time series. Experiments demonstrate that PPCA outperforms prior methods in terms of retained information analysis, classification, and CPU time consumption.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Information Systems
Christopher M. A. Bonenberger, Friedhelm Schwenker, Wolfgang Ertel, Markus Schneider
Summary: This paper presents a novel generalization of Principal Component Analysis (PCA) that allows for decorrelation of data based on desired correlation patterns. By generalizing the projection onto a multi-dimensional subspace, this method can incorporate known statistical dependencies between input variables, thereby enhancing overall performance. Additionally, the paper discusses the role of this method in relation to other well-known time series analysis techniques.
Article
Computer Science, Artificial Intelligence
Chenglong Li, Xiaobin Yang, Guohao Wang, Aihua Zheng, Chang Tan, Jin Tang
Summary: License plate recognition is crucial in various practical applications, however, recognizing license plates of large vehicles is challenging due to low resolution, contamination, low illumination, and occlusion. To address this problem, a novel data generation framework based on the Disentangled Generation Network is proposed to ensure the generation diversity and integrity for robust enlarged license plate recognition.
COMPUTER VISION AND IMAGE UNDERSTANDING
(2024)
Article
Computer Science, Artificial Intelligence
Sara Casao, Alvaro Serra-Gomez, Ana C. Murillo, Wendelin Bohmer, Javier Alonso-Mora, Eduardo Montijano
Summary: This paper presents a hybrid camera system that combines static and mobile cameras, exploiting the cooperation between tracking and control modules to achieve high-level scene understanding. The static camera network provides global awareness, while the mobile cameras enhance the information about the people on the scene.
COMPUTER VISION AND IMAGE UNDERSTANDING
(2024)
Article
Computer Science, Artificial Intelligence
Anh-Dzung Doan, Bach Long Nguyen, Surabhi Gupta, Ian Reid, Markus Wagner, Tat-Jun Chin
Summary: To ensure reliable object detection in autonomous systems, the detector needs to adapt to changes in appearance caused by environmental factors. We propose a selective adaptation approach using domain gap as a criterion to improve the efficiency of the detector's operation.
COMPUTER VISION AND IMAGE UNDERSTANDING
(2024)
Article
Computer Science, Artificial Intelligence
Tianhong Dai, Wei Li, Xilei Cao, Jianzhuang Liu, Xu Jia, Ales Leonardis, Youliang Yan, Shanxin Yuan
Summary: This study proposes a novel frequency-guided deep neural network (FHDRNet) for high dynamic range (HDR) imaging from multiple low dynamic range (LDR) images, aiming to address ghosting artifacts. By conducting HDR fusion in the frequency domain, the network utilizes low-frequency signals to remove specific ghosting artifacts and high-frequency signals to preserve details. Extensive experiments demonstrate that this approach achieves state-of-the-art performance.
COMPUTER VISION AND IMAGE UNDERSTANDING
(2024)
Article
Computer Science, Artificial Intelligence
Guobin Li, Reyer Zwiggelaar
Summary: Breast cancer is the most commonly diagnosed female malignancy worldwide. Recent developments in deep convolutional neural networks have shown promising performance for breast cancer detection and classification. However, biased features can be learned due to variations in appearance and small datasets. To address this issue, a densely connected convolutional network (DenseNet) was trained using texture features representing different physical morphological representations as inputs. The use of connectivity estimation and nearest neighbors improved the network's unbiased prediction. The approach achieved higher diagnostic accuracy and provided visual explanations for model predictions.
COMPUTER VISION AND IMAGE UNDERSTANDING
(2024)
Article
Computer Science, Artificial Intelligence
Yuezun Li, Cong Zhang, Honggang Qi, Siwei Lyu
Summary: Deep Neural Networks (DNNs) are vulnerable to adversarial perturbations, limiting their applicability in safe-critical scenarios. To address this, a new method called AdaNI is proposed to increase feature randomness through adaptive noise injection, improving adversarial robustness. Extensive experiments demonstrate the efficacy of AdaNI against various white-box and black-box attacks, as well as its applicability in DeepFake detection.
COMPUTER VISION AND IMAGE UNDERSTANDING
(2024)
Article
Computer Science, Artificial Intelligence
Chengyin Hu, Weiwen Shi, Ling Tian, Wen Li
Summary: In this study, we introduce a pioneering black-box light-based physical attack called Adversarial Neon Beam (AdvNB). Our method excels in attack modeling, efficient attack simulation, and robust optimization, striking a balance between robustness and efficiency. Through rigorous evaluation, we achieve impressive attack success rates in both digital and real-world scenarios. AdvNB demonstrates its stealthiness through comparisons with baseline samples and consistently achieves high success rates when targeting advanced DNN models.
COMPUTER VISION AND IMAGE UNDERSTANDING
(2024)
Article
Computer Science, Artificial Intelligence
Hang Wang, Zhenyu Ding, Cheng Cheng, Yuhai Li, Hongbin Sun
Summary: Learning-based super resolution has made remarkable progress in improving image quality, but the performance decreases when the degradation kernel changes. Blind SR networks can estimate the degradation kernel and adapt well in realistic scenarios, improving performance and runtime. This paper proposes a design that imposes constraints for the kernel estimation network in both the image domain and kernel domain, resulting in high-quality images and efficient runtime.
COMPUTER VISION AND IMAGE UNDERSTANDING
(2024)
Article
Computer Science, Artificial Intelligence
Yuantao Chen, Runlong Xia, Kai Yang, Ke Zou
Summary: This paper proposes an improved image inpainting network using a multi-scale feature module and improved attention module. The network addresses issues in deep learning-based image inpainting algorithms, such as information loss in deep level features and the neglect of semantic features. The proposed network generates better inpainting results by reducing information loss and enhancing the ability to restore texture and semantic features.
COMPUTER VISION AND IMAGE UNDERSTANDING
(2024)
Article
Computer Science, Artificial Intelligence
Yi-Tung Chan
Summary: This study proposes a novel maritime background subtraction method based on ensemble learning theory to address the challenges posed by dynamic marine environments and noise, improving the detection accuracy and enhancing maritime transportation security for autonomous ships in open waters.
COMPUTER VISION AND IMAGE UNDERSTANDING
(2024)
