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20th International Conference on Automation and Computing (ICAC)

作者： Zeng, W. Jiang, X. Lou, S. Scott, P. J. Univ Huddersfield Sch Comp & Engn EPSRC Ctr Innovat Mfg Adv Metrol Huddersfield HD1 3DH W Yorkshire England

ISBN: (纸本)9781909522022

Filtration is very important for surface texture *** filters that include linear and robust filters are recommended by ISO/TS 16610-22 and 32. In this paper, a general algorithm for both the linear and the robust spline filters is presented. The main advantage of the proposed algorithm is its ability to produce different types of filters only by choosing different M-estimators as shown in this note. This proposed algorithm relies on the Cholesky decomposition technique with an improved memory management procedure. Experimental and simulated results show that the proposed algorithm is fast, efficient and stable.

关键词： Surafce characterisation Surface texture fast algorithm Spline filter

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A fast singular boundary method for 3D Helmholtz equation

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2019年第2期77卷 525-535页

作者： Li, Weiwei Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255049 Peoples R China

This paper presents a fast singular boundary method (SBM) for three-dimensional (3D) Helmholtz equation. The SBM is a boundary-type meshless method which incorporates the advantages of the boundary element method (BEM) and the method of fundamental solutions (MFS). It is easy-to-program, and attractive to the problems with complex geometries. However, the SBM is usually limited to small-scale problems, because of the operation count of O(N-3) with direct solvers or O(N-2) with iterative solvers, as well as the memory requirement of O(N-2). To overcome this drawback, this study makes the first attempt to employ the precorrected-FFT (PFFT) to accelerate the SBM matrix- vector multiplication at each iteration step of the GMRES for 3D Helmholtz equation. Consequently, the computational complexity can be reduced from O(N-2) to O(NlogN) or O(N). Three numerical examples are successfully tested on a desktop computer. The results clearly demonstrate the accuracy and efficiency of the developed fast PFFT-SBM strategy. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Singular boundary method Precorrected-FFT fast algorithm Three-dimensional Helmholtz problems

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fast Morphology algorithm with Parallel Processing Structures 4

Fast Morphology Algorithm with Parallel Processing Structure...

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International Conference on Audio, Language and Image Processing

作者： Ding, Jian-Jiun Lee, Pin-Xuan Natl Taiwan Univ Grad Inst Commun Engn Taipei Taiwan

ISBN: (纸本)9781479939039

Morphology, including erosion and dilation, is important for many image processing applications, such as shape analysis, pattern recognition, denoising, and segmentation. In this paper, an efficient way to perform erosion and dilation operations is proposed. Since the proposed algorithm deals with each row and column separately, it can be implemented in a parallel processing structure, which can significantly reduce the computation time. Moreover, recursive structures can also be adopted in the proposed algorithm. In addition to less computation time, the memory requirement of the proposed algorithm is also very less. The proposed algorithm is suitable for any structuring element of the symmetric and convex form. When using different structuring element, only a very small part of the structure should be adjusted. The proposed algorithm can much improve the efficiency of morphology and is suitable for both software and hardware implementation.

关键词： morphology erosion dilation fast algorithm parallel processing

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fast Graph Fourier Transforms Based on Graph Symmetry and Bipartition

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2019年第18期67卷 4855-4869页

作者： Lu, Keng-Shih Ortega, Antonio Univ Southern Calif Dept Elect & Comp Engn Los Angeles CA 90089 USA

The graph Fourier transform (GFT) is an important tool for graph signal processing, with applications ranging from graph-based image processing to spectral clustering. However, unlike the discrete Fourier transform, the GFT typically does not have a fast algorithm. In this work, we develop new approaches to accelerate the GFT computation. In particular, we show that Haar units (Givens rotations with angle pi/4) can be used to reduce GFT computation cost when the graph is bipartite or satisfies certain symmetry properties based on node pairing. We also propose a graph decomposition method based on graph topological symmetry, which allows us to identify and exploit butterfly structures in stages. This method is particularly useful for graphs that are nearly regular or have some specific structures, e.g., line graphs, cycle graphs, grid graphs, and human skeletal graphs. Though butterfly stages based on graph topological symmetry cannot be used for general graphs, they are useful in applications, including video compression and human action analysis, where symmetric graphs, such as symmetric line graphs and human skeletal graphs, are used. Our proposed fast GFT implementations are shown to reduce computation costs significantly, in terms of both number of operations and empirical runtimes.

关键词： Graph Fourier transform fast algorithm graph signal processing symmetric graph bipartite graph

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fast and accurate computation of Racah moment invariants for image classification

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PATTERN RECOGNITION 2019年 91卷 100-110页

作者： Benouini, Rachid Batioua, Imad Zenkouar, Khalid Zahi, Azeddine El Fadili, Hakim Qjidaa, Hassan Univ Sidi Mohamed Ben Abdellah Fac Sci & Technol Lab Intelligent Syst & Applicat LSIA BP 2202Route Immouzer Fes 30003 Morocco Univ Sidi Mohamed Ben Abdellah Ecole Natl Sci Appl Fez Fes Morocco Sidi Mohamed Ben Abdellah Univ Fac Sci Dhar El Mehraz LESSI Fes Morocco

In this paper, a new set of moment invariants, named Racah Moment Invariants (RMI), is introduced in the field of image analysis. This new set can be used to describe pattern feature independently of Rotation, Scaling and Translation transforms. Moreover, new fast and accurate algorithm, using recursive method, is developed for accelerating the computation time of the newly proposed invariants, as well as, for enhancing their numerical stability. Subsequently, several experiments have been performed. Initially, the numerical stability and computational cost are depicted. Secondly, the global and local features extraction are clearly illustrated. Then, invariability property and noise robustness are investigated. Finally, the discrimination power and the classification accuracy of the proposed invariants are extensively tested on several publicly available databases. The presented theoretical and experimental results, clearly show that the proposed method can be extremely useful in the fields of image classification. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Racah moment invariants Racah polynomials fast algorithm Accurate computation Direct method Recursive method Image classification Pattern recognition

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Orthogonal Dual Graph-Regularized Nonnegative Matrix Factorization for Co-Clustering

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JOURNAL OF SCIENTIFIC COMPUTING 2021年第3期87卷 66-66页

作者： Tang, Jiayi Wan, Zhong Cent South Univ Sch Math & Stat Changsha Peoples R China

Nonnegative Matrix Factorization (NMF) has received great attention in the era of big data, owing to its roles in efficiently reducing data dimension and producing feature-based data representation. In this paper, we first propose two new NMF optimization models, called an orthogonal dual graph regularized nonnegative matrix factorization (ODGNMF) method and its modified version: an orthogonal dual graph regularized nonnegative matrix tri-factorization (ODGNMTF) method. Compared with the existing models, our models can preserve the geometrical structures of data manifold and feature manifold by constructing two graphs, and ensure the orthogonality of factor matrices such that they have better NMF performance. Then, two efficient algorithms are developed to solve the models, and the convergence theory of the algorithms is established. Numerical tests by applying our algorithms to mine randomly generated data sets and well-known public databases demonstrate that ODGNMF and ODGNMTF have better numerical performance than the state-of-the-art algorithms in view of computational cost, robustness, sensitivity and sparseness.

关键词： Nonnegative matrix factorization fast algorithm Machine learning Optimization model

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Low-Complexity Intra Prediction for Screen Content Coding by Convolutional Neural Network 52

Low-Complexity Intra Prediction for Screen Content Coding by...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Kuang, Wei Chan, Yui-Lam Tsang, Sik-Ho Hong Kong Polytech Univ Dept Elect & Informat Engn Hung Hom Kowloon Hong Kong Peoples R China

ISBN: (纸本)9781728133201

Screen content coding (SCC) is developed to encode screen content videos, and it is an extension of High Efficiency Video Coding (HEVC). Since screen content videos contain computer-generated content that shows special characteristics, SCC adopts the new Intra Block Copy mode and Palette mode besides the HEVC based Intra mode to improve the coding efficiency. However, the exhaustive mode searching process makes the SCC encoder computational expensive. In this paper, a low-complexity intra prediction algorithm is proposed by the convolutional neural network (CNN). The proposed network skips unnecessary coding units (CUs) and mode candidates by imitating the behavior of the original SCC encoder. The network first decides if a CU size should be checked by analyzing global features, and it decides which mode should be checked by analyzing the local features. Experimental results show that the proposed algorithm achieves 53.44% computational complexity reduction on average with 1.94% Bjontegaard delta bitrate loss under All Intra configuration.

关键词： Screen Content Coding (SCC) High Efficiency Video Coding (HEVC) Convolutional Neural Network fast algorithm

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fast High-Dimensional Kernel Filtering

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IEEE SIGNAL PROCESSING LETTERS 2019年第2期26卷 377-381页

作者： Nair, Pravin Chaudhury, Kunal Narayan Indian Inst Sci Dept Elect Engn Bengaluru 560012 India

The bilateral and nonlocal means filters are instances of' kernel-based filters that are popularly used in image processing. It was recently shown that fast and accurate bilateral filtering of grayscale images can he performed using a low-rank approximation of the kernel matrix. More specifically, based on the eigendecomposition of the kernel matrix, the overall filtering was approximated using spatial convolutions, for which efficient algorithms are available. Unfortunately, this technique cannot be scaled to high-dimensional data such as color and hyperspectral images. This is simply because one needs to compute/store a large matrix and perform its eigendecomposition in this case. We show how this problem can be solved using the Nystrom method, which is generally used for approximating the eigendecomposition of large matrices. The resulting algorithm can also be used for nonlocal means filtering. We demonstrate the effectiveness of our proposal for bilateral and nonlocal means filtering of color and hyperspectral images. In particular, our method is shown to be competitive with state-of-the-art fast algorithms, and moreover, it comes with a theoretical guarantee on the approximation error.

关键词： Kernel filter Nystrom method approximation fast algorithm error bound

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fast HEVC to SCC Transcoder by Early CU Partitioning Termination and Decision Tree-Based Flexible Mode Decision for Intra-Frame Coding

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IEEE ACCESS 2019年 7卷 8773-8788页

作者： Kuang, Wei Chan, Yui-Lam Tsang, Sik-Ho Siu, Wan-Chi Hong Kong Polytech Univ Ctr Signal Proc Dept Elect & Informat Engn Hong Kong Peoples R China

Screen content coding (SCC) was developed to enhance High Efficiency Video Coding (HEVC) for encoding screen content videos. However, HEVC has dominated the market for many years, and it leaves many legacy screen content videos encoded by HEVC. Therefore, it is desired that the legacy screen content videos are migrated from HEVC to SCC to improve the coding efficiency. This paper presents a fast transcoding algorithm by analyzing various features from four categories. They are the features from the HEVC decoder, static features, dynamic features, and spatial features. First, the coding unit (CU) depth level collected from the HEVC decoder is utilized to early terminate the CU partition in SCC. Second, a flexible encoding structure is proposed to make early mode decisions with the help of various features. On the one hand, high decision accuracy is achieved because mode decision is considered from different aspects by utilizing features from more than one category. On the other hand, high computational complexity is reduced because the flexible structure considers the decision of each mode separately. The experimental results show that the proposed algorithm provides 51.24% and 54.65% re-encoding time reduction with 1.32% and 1.25% negligible Bjontegaard delta bitrate loss for YUV 4:2:0 and YUV 4:4:4 screen content sequences using all-intra configuration, respectively.

关键词： Transcoding screen content coding (SCC) High Efficiency Video Coding (HEVC) fast algorithm machine learning

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A fast INTRA CODING algorithm FOR SPATIAL SCALABILITY IN SHVC 25

A FAST INTRA CODING ALGORITHM FOR SPATIAL SCALABILITY IN SHV...

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25th IEEE International Conference on Image Processing (ICIP)

作者： Lu, Xin Yu, Chang Gu, Yanfeng Martin, Graham Harbin Inst Technol Sch Elect & Informat Engn Harbin Heilongjiang Peoples R China Univ Warwick Dept Comp Sci Coventry W Midlands England

ISBN: (纸本)9781479970612

Scalable High Efficiency Video Coding (SHVC) provides high compression efficiency at the expense of considerable computational complexity. In this paper, a fast algorithm is proposed to reduce the computational complexity of SHVC intra coding. The coding depth information, texture complexity, and spatio-temporal correlation are jointly used to achieve a faster depth decision process. The mode dependency between the base layer and the enhancement layer is combined with the temporal correlation to simplify the mode decision process. Experimental results demonstrate that the proposed scheme saves encoding time by up to 59% compared with the SHM12.0 encoder with negligible degradation in Rate Distortion (RD).

关键词： SHVC Intra coding fast algorithm

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