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Overall filtering Algorithm for Multiscale Noise Removal From Point Cloud Data

IEEE ACCESS 2021年 9卷 110723-110734页

作者： Ren, Yujuan Li, Tianzi Xu, Jikun Hong, Wenwen Zheng, Yanchao Fu, Biao Henan Polytech Univ Sch Surveying Mapping & Land Informat Engn Jiaozuo 454000 Henan Peoples R China Zhengzhou Univ Sch Water Conservancy Sci & Engn Zhengzhou 450001 Peoples R China Nat Resources Bur & Planning Bur Hebi City Hebi 458030 Peoples R China

The multiscale noise in the 3D point cloud data of rock surfaces which collected by 3D scanners has a significant influence on the exploration of rock surface morphology. To this end, this paper proposes a multiscale noise removal overall filtering algorithm. The specific processing procedure of the algorithm is as follows. First, a weighted principal component analysis is performed on point cloud data, i.e., the neighboring point distance is used as a weight in the principal component analysis, the covariance feature matrix of the weighted point is estimated, and the eigenvector corresponding to the lowest eigenvalue is used as the normal vector of the point cloud data. Second, in the weighted principal component analysis, estimating three eigenvalues corresponding to the Eigen matrix of the point cloud data, the ratio of the eigenvalue corresponding to the normal vector to the sum of three eigenvalues is used as the surface change factor. For the sample point, if the surface change factor of one sample point is less than the average value of the surface change factor of all sample points in the neighborhood, the sample point belongs to a flat area;otherwise, it belongs to a mutation area. Finally, in order to achieve multiscale noise removal, statistical filtering algorithm is used to remove large scale noise in flat area, additionally bilateral filtering algorithm is adopted to remove small scale noise in mutation area. In the experiments, the improved principal component analysis is combined with the overall filtering algorithm to accurately estimate the eigenvalues of the point cloud data points. After that, the eigenvalues of the sample points are used to distinguish between flat area and mutation area, so as to consider large scale noise and small scale noise. From the experimental results, it can be seen that overall filtering algorithm can consider both large scale and small scale noise and can remove noise from the point cloud data of rock samples. Visual

关键词： Three-dimensional displays Surface morphology Surface treatment Licenses filtering algorithms Rocks Noise reduction 3D scanner point cloud data multiscale noise statistical filtering bilateral filtering

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EFFICIENT RECURSIVE IN ORDER LEAST-SQUARES FIR filtering AND PREDICTION

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1985年第5期33卷 1175-1187页

作者： KALOUPTSIDIS, N CARAYANNIS, G MANOLAKIS, D KOUKOUTSIS, E UNIV ATHENS DEPT PHYSDIV ELECTRATHENSGREECE NATL TECH UNIV ATHENS DEPT ELECT ENGNGR-147 ATHENSGREECE

This paper is concerned with the efficient determination of the optimum, in the least squares sense, FIR filter on the basis of data samples of the input and desired response signals, by procedures recursive in the filter order. This situation typically arises when no a priori statistics are available and the system order is not known. The general multiinput-multioutput (multichannel) case is considered here and a fast algorithm is presented requiring for single channel signals approximately 2S + 15m multiplications (mps) per order m, S being the number of samples. In the special case of linear prediction it calls for about S + 12m mps. Hence it offers a computational reduction of 5m and 2m mps in comparison to the methods of Marple [1] and Morf et al. [2], respectively. Additionally, the proposed scheme is inherently symmetric and is suited very well to initialization of fast sequential algorithms as well as algorithms searching for the optimum lag filter.

关键词： Finite impulse response filter Least squares methods Signal processing algorithms Nonlinear filters filtering algorithms System identification Speech processing Equations Signal processing Transversal filters

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Efficient deblocking with coefficient regularization, shape-adaptive filtering, and quantization constraint

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IEEE TRANSACTIONS ON MULTIMEDIA 2008年第5期10卷 735-745页

作者： Zhai, Guangtao Zhang, Wenjun Yang, Xiaokang Lin, Weisi Shanghai Jiao Tong Univ Inst Image Communicat & Informat Proc Shanghai 200240 Peoples R China Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

We propose an effective deblocking scheme with extremely low computational complexity. The algorithm involves three parts: local ac coefficient regularization (ACR) of shifted blocks in the discrete cosine transform (DCT) domain, block-wise shape adaptive filtering (BSAF) in the spatial domain, and quantization constraint (QC) in the DCT domain. The DCT domain ACR suppresses the grid noise (blockiness) in monotone areas. The spatial-domain BSAF alleviates the staircase noise along the edge, and the ringing near the edge and the corner outliers. The narrow quantization constraint set is imposed to prevent possible oversmoothing and improve PSNR performance. Extensive simulation results and comparative studies are provided to justify the effectiveness and efficiency of the proposed deblocking algorithm.

关键词： Quantization Discrete cosine transforms PSNR Noise shaping Image communication Information processing Computational complexity filtering algorithms Shape Adaptive filters

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A quadrature-based method of moments for nonlinear filtering

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AUTOMATICA 2009年第5期45卷 1291-1298页

作者： Xu, Yunjun Vedula, Prakash Univ Cent Florida Dept Mech Mat & Aerosp Engn Orlando FL 32816 USA Univ Oklahoma Sch Aerosp & Mech Engn Norman OK 73019 USA

According to the nonlinear filtering theory, optimal estimates of a general continuous-discrete nonlinear filtering problem can be obtained by solving the Fokker-Planck equation coupled with a Bayesian, update rule. This procedure does not rely on linearizations of the dynamical and/or measurement models. However, the lack of fast and efficient methods for solving the Fokker-Planck equation presents challenges in real time nonlinear filtering problems. In this paper, a direct quadrature method of moments is introduced to solve the Fokker-Planck equation efficiently and accurately. This approach involves representation of the state conditional probability density function in terms of a finite collection of Dirac delta functions. The weights and locations (abscissas) in this representation are determined by moment constraints and modified using the Bayes' rule according to measurement updates. As demonstrated by numerical examples, this approach appears to be promising in the field of nonlinear filtering. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Nonlinear filtering Estimation Fokker-Planck equation Stochastic differential equation filtering algorithms

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Effective algorithms for regressor based adaptive infinite impulse response filtering

Effective algorithms for regressor based adaptive infinite i...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： E. Acar O. Arikan Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

To take advantage of fast converging multi-channel recursive least squares algorithms, we propose an adaptive IIR system structure consisting of two parts: a two-channel FIR adaptive filter whose parameters are updated by rotation-based multi-channel least squares lattice (QR-MLSL) algorithm, and an adaptive regressor which provides more reliable estimates to the original system output based on previous values of the adaptive system output and noisy observation of the original system output. Two different regressors are investigated and robust ways of adaptation of the regressor parameters are proposed. Based on an extensive set of simulations, it is shown that the proposed algorithms converge faster to more reliable parameter estimates than LMS type algorithms.

关键词： IIR filters filtering algorithms Adaptive filters Least squares approximation Finite impulse response filter Cost function Parameter estimation Least squares methods Adaptive systems Output feedback

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Surface Reconstruction: 3D Mesh filtering with Feature Preserving Bi-Adaptive algorithms

Surface Reconstruction: 3D Mesh Filtering with Feature Prese...

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Pierre Kraemer Marc Fournier Dominique Bechmann LSIIT Louis Pasteur University Strasbourg France ETS Quebec University Montreal Canada

In this paper we define two bi-adaptive mesh filtering algorithms for efficient features preserving restoration of noisy models acquired from 3D scanners. The new filters are based on 2D image processing adaptive algorithms extended to 3D mesh processing. We combine two adaptive algorithms to design more efficient bi-adaptive algorithms. The first filter is applied on the mesh triangles normal vector orientation and the second filter is applied on the vertices normal vector orientation. Both filters are applied on adaptively selected local neighborhood based on a threshold angle between normal vectors. Then the algorithm to update the vertices position is also adaptive according to a local variance evaluation compared to a noise variance threshold. We compare our two bi-adaptive algorithms results to other filtering algorithms according to reliable error metrics and show the new filters outperform previous features preserving and adaptive mesh denoising methods.

关键词： Surface reconstruction filtering algorithms Adaptive filters Signal processing algorithms Noise reduction Algorithm design and analysis Adaptive algorithm Laplace equations Image processing Nonlinear filters

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A NEW RECURSIVE PSEUDO-LEAST-SQUARES ALGORITHM FOR ARMA filtering AND MODELING .2.

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1992年第11期40卷 2775-2783页

作者： PRASAD, S JOSHI, SD Department of Electrical Engineering Indian Institute of Technology New Delhi India

A recursive algorithm for ARMA filtering has been developed in a companion paper. These recursions are seen to have a lattice-like filter structure. The ARMA parameters, however, are not directly available from the coefficients of this filter. This paper, therefore, takes up the problem of identification of the ARMA model from the coefficients of this filter. Two new update relations for certain pseudoinverses are derived here which are used to obtain a recursive least squares algorithm for AR parameter estimation. Two methods for the estimation of the MA parameters are also presented here. Numerical results are presented to demonstrate the usefulness of the proposed algorithms.

关键词： Least squares methods filtering algorithms Lattices Difference equations Vectors Standards development Nonlinear filters Adaptive filters Stochastic processes Adaptive algorithm

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Interval Kalman filtering

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1997年第1期33卷 250-259页

作者： Chen, GR Wang, JR Shieh, LS University of Houston USA

The classical Kalman filtering technique is extended to interval linear system with the same statistical assumptions on noise, for which the classical technique is no longer applicable. Necessary interval analysis, particularly the notion of interval expectation, is reviewed and introduced. The interval Kalman filter (IKF) is then derived, which has the same structure as the classical algorithm, using no additional analysis or computation from such as H-infinity-mathematics. A suboptimal IKF is suggested next, for the purpose of real-time implementation. Finally, computer simulations are shown to compare the new interval Kalman filtering algorithm with the classical Kalman filtering scheme and some other existing robust Kalman filtering methods.

关键词： Kalman filters filtering algorithms Nonlinear filters Robustness Linear systems Algorithm design and analysis Computer simulation Military computing Random variables Vectors

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Multivariate Fast Iterative filtering for the Decomposition of Nonstationary Signals

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 1521-1531页

作者： Cicone, Antonio Pellegrino, Enza Univ Aquila DISIM I-67100 Laquila Italy INAF Ist Astrofis & Planetol Spaziali I-00133 Rome Italy Ist Nazl Geofis & Vulcanol I-00143 Rome Italy Univ Aquila DIIIE I-67040 Laquila Italy

In this work, we present a new technique for the decomposition of multivariate data, which we call Multivariate Fast Iterative filtering (MvFIF) algorithm. We study its properties, proving rigorously that it converges in finite time when applied to the decomposition of any kind of multivariate signal. We test MvFIF performance using a wide variety of artificial and real multivariate signals, showing its ability to: separate multivariate modulated oscillations;align frequencies along different channels;produce a quasi-dyadic filterbank when decomposing white Gaussian noise;decompose the signal in a quasi-orthogonal set of components;being robust to noise perturbation, even when the number of channels is increased considerably. Finally, we compare it and its performance with the main methods developed so far in the literature, proving that MvFIF produces, without any a priori assumption on the signal under investigation and in a fast and reliable manner, a uniquely defined decomposition of any multivariate signal.

关键词： Signal processing algorithms filtering Iterative algorithms Optimization Frequency modulation Convergence Wavelet transforms Adaptive signal processing filtering algorithms array signal processing iterative methods

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Modified Huber M-Estimate Function-Based Distributed Constrained Adaptive filtering Algorithm Over Sensor Network

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IEEE SENSORS JOURNAL 2022年第20期22卷 19567-19582页

作者： Xu, Wenjing Zhao, Haiquan Zhou, Lijun Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

Most of the existing distributed adaptive filtering algorithms over wireless sensor networks (WSNs) are developed, aiming to solve unconstrained network optimization problems. However, in practice, the weight coefficients of the filter may need to satisfy a set of linear equations. Thus, a distributed adaptive algorithm that can solve the sensor network optimization problem under constraints is needed. Considering the possible impulsive interference in the observed signals, a novel robust distributed constrained adaptive algorithm called diffusion constrained least mean M-estimate (D-CLMM) is proposed by using the modified Huber function (MHF), which endows the network robustness to impulsive noise. The transient, steady-state performances and stability of the proposed D-CLMM are studied with the aid of some commonly used assumptions and verified by computer simulations. Moreover, the effectiveness of D-CLMM is verified in distributed parameter estimation and beamforming applications in non-Gaussian noise environments.

关键词： Sensors Optimization Wireless sensor networks Adaptive algorithms filtering algorithms Adaptive systems Robustness Distributed constrained adaptive filtering M-estimate non-Gaussian noise performance analysis robust algorithm wireless sensor network (WSN)

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