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DA-IRRK: data-adaptive Iteratively Reweighted Robust Kernel-Based Approach for Back-End Optimization in Visual SLAM

SENSORS 2025年第8期25卷 2529-2529页

作者： Hu, Zhimin Cheng, Lan Wei, Jiangxia Xu, Xinying Zhang, Zhe Yan, Gaowei Taiyuan Univ Technol Elect & Power Engn Yingxi Campus79 Yingze West St Taiyuan 030024 Peoples R China

Back-end optimization is a key process to eliminate the cumulative error in Visual Simultaneous Localization and Mapping (VSLAM). Existing VSLAM frameworks often use kernel function-based back-end optimization methods. However, these methods typically rely on fixed kernel parameters based on the chi-square test, assuming Gaussian-distributed reprojection errors. In practice, though, reprojection errors are not always Gaussian, which can reduce robustness and accuracy. Therefore, we propose a data-adaptive iteratively reweighted robust kernel (DA-IRRK) approach, which combines median absolute deviation (MAD) with iteratively reweighted strategies. The robustness parameters are adaptively adjusted according to the MAD of reprojection errors, and the Huber kernel function is used to demonstrate the implementation of the back-end optimization process. The method is compared with other robust function-based approaches via the EuRoC dataset and the KITTI dataset, showing adaptability across different VSLAM frameworks and demonstrating significant improvements in trajectory accuracy on the vast majority of dataset sequences. The statistical analysis of the results from the perspective of reprojection error indicates DA-IRRK can tackle non-Gaussian noises better than the compared methods.

关键词： visual SLAM back-end optimization median absolute deviation data-adaptive iteratively reweighted robust kernel

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data-adaptive Similarity Measures for B-mode Ultrasound Images Using Robust Noise Models

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2020年第6期14卷 1244-1254页

作者： Ouzir, Nora Ollila, Esa Vorobyov, Sergiy A. Aalto Univ Dept Signal Proc & Acoust Espoo Finland

Ultrasound imaging (UI) is characterized by the presence of multiplicative speckle noise and various acquisition artefacts. Designing ultrasound (US) similarity measures thus requires a particular attention. In the specific context of motion estimation, incorporating US characteristics does not only benefit traditional methods but also learning-based approaches, which are highly sensitive to the quality of training data. Deriving similarity measures from a maximum likelihood (ML) perspective allows us to take these specificities into account. As opposed to the classical Rayleigh modelling, the proposed similarity measures incorporate more realistic scattering conditions, such as, varying speckle densities and shadowing. Specifically, the deviations from the Rayleigh statistics are modelled using the t-distribution for the complex radio-frequency (RF) signals and the Nakagami-Gamma (NG) compound model for the echo amplitudes. Furthermore, the model parameters are learnt patch-wise, which leads to data-adaptive similarity measures. The proposed criteria are investigated in the context of motion estimation using synthetic, phantom, as well as 2D and 3D in vivo images. The experimental results show an improvement in performance and robustness in comparison to the classical Rayleigh-based approach and state-of-the-art similarity measures.

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data-adaptive additive modeling

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STATISTICS IN MEDICINE 2019年第4期38卷 583-600页

作者： Petersen, Ashley Witten, Daniela Univ Minnesota Div Biostat MMC 303420 Delaware St SE Minneapolis MN 55455 USA Univ Washington Dept Biostat Seattle WA 98195 USA Univ Washington Dept Stat Seattle WA 98195 USA

In this paper, we consider fitting a flexible and interpretable additive regression model in a data-rich setting. We wish to avoid pre-specifying the functional form of the conditional association between each covariate and the response, while still retaining interpretability of the fitted functions. A number of recent proposals in the literature for nonparametric additive modeling are data adaptive, in the sense that they can adjust the level of flexibility in the functional fits to the data at hand. For instance, the sparse additive model makes it possible to adaptively determine which features should be included in the fitted model, the sparse partially linear additive model allows each feature in the fitted model to take either a linear or a nonlinear functional form, and the recent fused lasso additive model and additive trend filtering proposals allow the knots in each nonlinear function fit to be selected from the data. In this paper, we combine the strengths of each of these recent proposals into a single proposal that uses the data to determine which features to include in the model, whether to model each feature linearly or nonlinearly, and what form to use for the nonlinear functions. We establish connections between our approach and recent proposals from the literature, and we demonstrate its strengths in a simulation study.

关键词： additive model data-adaptive feature selection high-dimensional nonparametric regression sparse model

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Point Clouds Attribute Compression Using data-adaptive Intra prediction 33

Point Clouds Attribute Compression Using Data-Adaptive Intra...

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33rd IEEE International Conference on Visual Communications and Image Processing (IEEE VCIP)

作者： Zhang, Qi Shao, Yiting Li, Ge Peking Univ Shenzhen Grad Sch Sch Elect & Comp Engn Beijing Peoples R China

ISBN: (纸本)9781538644584

In recent years, 3D sensing and capture technologies have made constant progress, leading to point clouds with higher resolution and fidelity. Since most applications demand compact storage and fast transmission, the issue of how to compress point clouds efficiently becomes an intractable problem. While previous GFT-based solutions use the transform tool to decorrelate attributes directly, ignoring the overall attribute's data spatial redundancy, Graph Fourier Transform (GFT) has shown good performance on point cloud attribute compression. So, motivated by coding tools in traditional image and video coding, we propose a block-based data-adaptive intra prediction tool before graph transform processing to further reduce the redundancy. We adopt uniform quantizing and context-based arithmetic coding to get the final bitstream. Experimental results on different datasets demonstrate that our method improves the compression efficiency of other GFT-based schemes and has much better BD-BR performance than the state-of-the-art Region-adaptive Hierarchical Transform (RAHT) approach on most specified point cloud contents.

关键词： Point cloud attribute compression data-adaptive Intra prediction Graph transform Entropy coding

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The concordance filter: an adaptive model-free feature screening procedure

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COMPUTATIONAL STATISTICS 2024年第5期39卷 2413-2436页

作者： Cheng, Xuewei Li, Gang Wang, Hong Hunan Normal Univ Sch Math & Stat MOE LCSM Changsha Peoples R China Hunan Normal Univ Coll Hunan Prov Key Lab Appl Stat & Data Sci Changsha Peoples R China Cent South Univ Sch Math & Stat Changsha Peoples R China Univ Calif Los Angeles Sch Publ Hlth Los Angeles CA USA

A new model-free and data-adaptive feature screening procedure referred to as the concordance filter is developed for ultrahigh-dimensional data. The proposed method is based on the concordance filter which measures concordance between random vectors and can work adaptively with several types of predictors and response variables. We apply the concordance filter to deal with feature screening problems emerging from a wide range of real applications, such as nonparametric regression and survival analysis, among others. It is shown that the concordance filter enjoys the sure screening and rank consistency properties under weak regularity conditions. In particular, the concordance filter can still be powerful in the presence of censoring and heavy tails. We further demonstrate the superior performance of the concordance filter over existing screening methods by numerical examples and medical applications.

关键词： Concordance filter Sure independent screening High-dimensional data Model-free data-adaptive

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Two-Stage TMLE to reduce bias and improve efficiency in cluster randomized trials

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BIOSTATISTICS 2023年第2期24卷 502-517页

作者： Balzer, Laura B. van der Laan, Mark Ayieko, James Kamya, Moses Chamie, Gabriel Schwab, Joshua Havlir, Diane, V Petersen, Maya L. Univ Massachusetts Dept Biostat & Epidemiol 715 North Pleasant St Amherst MA 01003 USA Univ Calif Berkeley Div Biostat 2121 Berkeley Way Berkeley CA 94720 USA Kenya Govt Med Res Ctr Ctr Microbiol Res POB 54840 00200 Off Raila Odinga Way Nairobi Kenya Makerere Univ Dept Med POB 7475 Kampala Uganda Infect Dis Res Collaborat POB 7475 Kampala Uganda Univ Calif San Francisco Dept Med 995 Potrero Ave San Francisco CA 94143 USA

Cluster randomized trials (CRTs) randomly assign an intervention to groups of individuals (e.g., clinics or communities) and measure outcomes on individuals in those groups. While offering many advantages, this experimental design introduces challenges that are only partially addressed by existing analytic approaches. First, outcomes are often missing for some individuals within clusters. Failing to appropriately adjust for differential outcome measurement can result in biased estimates and inference. Second, CRTs often randomize limited numbers of clusters, resulting in chance imbalances on baseline outcome predictors between arms. Failing to adaptively adjust for these imbalances and other predictive covariates can result in efficiency losses. To address these methodological gaps, we propose and evaluate a novel two-stage targeted minimum loss-based estimator to adjust for baseline covariates in a manner that optimizes precision, after controlling for baseline and postbaseline causes of missing outcomes. Finite sample simulations illustrate that our approach can nearly eliminate bias due to differential outcome measurement, while existing CRT estimators yield misleading results and inferences. Application to real data from the SEARCH community randomized trial demonstrates the gains in efficiency afforded through adaptive adjustment for baseline covariates, after controlling for missingness on individual-level outcomes.

关键词： Clustered data Cluster randomized trials Covariate adjustment data-adaptive Double robust Group randomized trials Missing data Multi-level models Super Learner TMLE

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A low-complexity data-adaptive approach for premature ventricular contraction recognition

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SIGNAL IMAGE AND VIDEO PROCESSING 2014年第1期8卷 111-120页

作者： Li, Peng Liu, Chengyu Wang, Xinpei Zheng, Dingchang Li, Yuanyang Liu, Changchun Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Newcastle Univ Inst Cellular Med Freeman Hosp Newcastle Upon Tyne NE7 7DN Tyne & Wear England Shandong Univ Prov Hosp Dept Med Engn Jinan 250014 Peoples R China

Premature ventricular contraction (PVC) may lead to life-threatening cardiac conditions. Real-time automated PVC recognition approaches provide clinicians the useful tools for timely diagnosis if dangerous conditions surface in their patients. Based on the morphological differences of the PVC beats in the ventricular depolarization phase (QRS complex) and repolarization phase (mainly T-wave), two beat-to-beat template-matching procedures were implemented to identify them. Both templates were obtained by a probability-based approach and hence were fully data-adaptive. A PVC recognizer was then established by analyzing the correlation coefficients from the two template-matching procedures. Our approach was trained on 22 ECG recordings from the MIT-BIH arrhythmia database (MIT-BIH-AR) and then tested on another 22 nonoverlapping recordings from the same database. The PVC recognition accuracy was 98.2 %, with the sensitivity and positive predictivity of 93.1 and 81.4 %, respectively. To evaluate its robustness against noise, our approach was applied again to the above testing set, but this time, the ECGs were not preprocessed. A comparable performance was still obtained. A good generalization capability was also confirmed by validating our approach on an independent St. Petersburg Institute of Cardiological Technics database. In addition, our performance was comparable with these published complex approaches. In conclusion, we have developed a low-complexity data-adaptive PVC recognition approach with good robustness against noise and generalization capability. Its performance is comparable to other state-of-the-art methods, demonstrating a good potential in real-time application.

关键词： Electrocardiogram (ECG) Premature ventricular contraction (PVC) Low-complexity data-adaptive Template matching

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Advances in Deep Learning Methods for Visual Tracking:Literature Review and Fundamentals

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International Journal of Automation and computing 2021年第3期18卷 311-333页

作者： Xiao-Qin Zhang Run-Hua Jiang Chen-Xiang Fan Tian-Yu Tong Tao Wang Peng-Cheng Huang College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China

Recently,deep learning has achieved great success in visual tracking tasks,particularly in single-object *** paper provides a comprehensive review of state-of-the-art single-object tracking algorithms based on deep ***,we introduce basic knowledge of deep visual tracking,including fundamental concepts,existing algorithms,and previous ***,we briefly review existing deep learning methods by categorizing them into data-invariant and data-adaptive methods based on whether they can dynamically change their model parameters or ***,we conclude with the general components of deep *** this way,we systematically analyze the novelties of several recently proposed deep ***,popular datasets such as Object Tracking Benchmark(OTB)and Visual Object Tracking(VOT)are discussed,along with the performances of several deep ***,based on observations and experimental results,we discuss three different characteristics of deep trackers,i.e.,the relationships between their general components,exploration of more effective tracking frameworks,and interpretability of their motion estimation components.

关键词： Deep learning visual tracking data-invariant data-adaptive general components

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Improving Consistency of GNSS-IR Reflector Height Estimates between Different Frequencies Using Multichannel Singular Spectrum Analysis

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REMOTE SENSING 2023年第7期15卷 1779-1779页

作者： Lei, Jintao Li, Wenhao Zhang, Shengkai Guangxi Univ Sch Civil Engn & Architecture Nanning 530004 Peoples R China Wuhan Univ Chinese Antarctic Ctr Surveying & Mapping Wuhan 430079 Peoples R China Nanjing Tech Univ Sch Geomatics Sci & Technol Nanjing 211816 Peoples R China

Previous studies of GNSS-IR mainly focused on the legacy L1C signal;the potential of modernized signals (L2C and L5Q) has not yet been fully exploited. In this paper, we applied the Multichannel Singular Spectrum Analysis (M-SSA) method to extract common interference patterns from different frequencies simultaneously. The three-frequency (L1C, L2C, and L5Q) signal-to-noise ratio (SNR) measurements from a total of 840 satellite rising and setting arcs, occurring between day of year 250 to 279 in year 2020 and 2021, were used. By comparing GNSS-IR reflector heights obtained from the original and M-SSA-reconstructed SNR time series, we found that M-SSA significantly improves the between-frequency consistency, as shown by an increase in the values of R-squared of linear regression from (0.69, 0.67, 0.89) to (0.95, 0.96, 0.98), and a decrease in RMSE from (0.10 m, 0.10 m, 0.06 m) to (0.04 m, 0.04 m, 0.02 m) for S1C-S2C, S1C-S5Q, and S2C-S5Q pair, respectively. Our results validate (1) the effectiveness of the M-SSA method in extracting common interference patterns from multi-frequency SNR time series, and (2) the superiority of modernized civil signals L2C and L5Q over the legacy L1C signal in GNSS-IR studies. We also emphasize the important role that the L5 signal will play in future GNSS-IR research because of its compatibility and interoperability among different satellite navigation systems.

关键词： GNSS-IR M-SSA data-adaptive multi-frequency

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adaptive Differentially Private data Stream Publishing in Spatio-temporal Monitoring of IoT 38

Adaptive Differentially Private Data Stream Publishing in Sp...

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IEEE 38th International Performance Computing and Communications Conference (IPCCC)

作者： Wang, Teng Yang, Xinyu Ren, Xuebin Zhao, Jun Lam, Kwok-Yan Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9781728110257

Spatio-temporal monitoring of the Internet of Things (IoT) has enabled the development and proliferation of third-party computing services by extensively exploiting the massive amount of sensing data. In particular, continuously generated data stream are monitored in real-time and exploited to facilitate people's daily lives, such as traffic monitoring and epidemic prevention. In its simplest way of deployment, the direct publishing of various streams could seriously compromise the privacy of participating users. Hence, a more sophisticated scheme is needed to regulate the privately publishing of data streams, which may possibly require control to be applied dynamically. However, most existing solutions are non-adaptive to dynamic changes of the streams due to constraints of predefined parameters, thus are vulnerable to low data utility. In this paper, we present AdaPub, a data-adaptive framework for infinite multi-dimensional stream real-time publishing with omega-event differential privacy while ensuring high data utility. Without predefining the parameters, AdaPub could learn and update the parameters that reflect the spatio-temporal correlations of the stream in a data-adaptive manner. Specifically, we propose two modules DimParti and AdaCluster which are seamlessly incorporated into AdaPub to simultaneously learn dimension correlations and time correlations in a data-adaptive way, thus greatly improving the data utility of the sanitized streams. Extensive experiments on real-world datasets demonstrate that our solution substantially outperforms state-of-the-art solutions with much lower errors while achieving strong privacy guarantees.

关键词： Multi-dimensional stream publishing data-adaptive differential privacy correlation learning data utility

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