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Texture-Aware Network for Enhancing Inner Smoke Representation in Visual Smoke Density Estimation

IET Computer Vision 2025年第1期19卷

作者： Xia, Xue Peng, Yajing Li, Zichen Shi, Jinting Fang, Yuming School of Computing and Artificial Intelligence Jiangxi University of Finance and Economics Nanchang China Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing Nanchang China School of Vocational Education and Technology Jiangxi Agricultural University Nanchang China

Smoke often appears before visible flames in the early stages of fire disasters, making accurate pixel-wise detection essential for fire alarms. Although existing segmentation models effectively identify smoke pixels, they generally treat all pixels within a smoke region as having the same prior probability. This assumption of rigidity, common in natural object segmentation, fails to account for the inherent variability within smoke. We argue that pixels within smoke exhibit a probabilistic relationship with both smoke and background, necessitating density estimation to enhance the representation of internal structures within the smoke. To this end, we propose enhancements across the entire network. First, we improve the backbone by adaptively integrating scene information into texture features through separate paths, enabling smoke-tailored feature representation for further exploit. Second, we introduce a texture-aware head with long convolutional kernels to integrate both global and orientation-specific information, enhancing representation for intricate smoke structure. Third, we develop a dual-task decoder for simultaneous density and location recovery, with the frequency-domain alignment in the final stage to preserve internal smoke details. Extensive experiments on synthetic and real smoke datasets demonstrate the effectiveness of our approach. Specifically, comparisons with 17 models show the superiority of our method, with mean IoU improvements of 4.88%, 2.63%, and 3.17% on three test sets. (The code will be available on https://***/xia-xx-cv/TANet_smoke). © 2025 The Author(s). IET Computer Vision published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Image segmentation

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Conservative mixed Discontinuous Galerkin finite element method with full decoupling strategy for incompressible MHD problems

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Communications in Nonlinear Science and Numerical Simulation 2025年 147卷

作者： Huang, Huayi Huang, Yunqing Tang, Qili Yin, Lina Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China National Center for Applied Mathematics in Hunan Xiangtan University Xiangtan411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan411105 China Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China

In this research, we propose a mixed Discontinuous Galerkin (DG) finite element method (FEM) for the numerical solution of incompressible magnetohydrodynamics (MHD) problems, ensuring the divergence-free condition for the velocity field. Our proposed methodology is characterized by its energy stability. Subsequently, we construct a novel finite element discretization scheme, for which we establish the theoretical framework of energy stability. Furthermore, we incorporate a preconditioning strategy that facilitates the development of an alternative positive definite, fully decoupled linearization approach. A significant advantage of this scheme is its ability to circumvent the complexities associated with solving saddle point problems at each time step. We demonstrate the uniqueness and convergence of the solutions within this fully decoupled, block-structured linear system. This theoretical validation is complemented by a series of numerical experiments that substantiate the effectiveness and accuracy of our proposed method. © 2025 Elsevier B.V.

关键词： Finite element method

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A Bi-selection Method Based on Consistent Matrix for Large-Scale Datasets

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IEEE Transactions on Fuzzy Systems 2025年

作者： Quan, Jinsheng Qiao, Fengcai Yang, Tian Shen, Shuo Qian, Yuhua Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Institute of Interdisciplinary Studies Changsha410081 China National University of Defense Technology College of Advanced Interdisciplinary Studies Changsha410081 China Shanxi University Institute of Big Data Science and Industry Taiyuan030006 China

Biselection (feature and sample selection) enhances the efficiency and accuracy of machine learning models when handling large-scale data. Fuzzy rough sets, an uncertainty mathematics model known for its excellent interpretability, are widely used in machine learning, particularly for feature selection. While the consistent matrix has significantly improved the computational efficiency and scalability of feature selection, like most fuzzy rough set-based methods, it focuses only on feature selection and seldom incorporates sample selection. This feature-centric approach can limit classification performance, particularly in noisy and large-scale datasets where both features and samples require judicious selection. To overcome these limitations, this article explores the integration of sample selection with feature selection. First, we introduce a β-consistent granulation method to generate more accurate and concise fuzzy information granules, reducing the impact of noisy samples. In addition, a novel membership function is employed to distinguish noise while simultaneously evaluating both features and samples. As a result, a biselection algorithm with linear computation complexity is proposed to select high-quality features and samples. Numerical experiments demonstrate that, compared to eleven representative algorithms, our proposed method achieves an average accuracy improvement of 9.66% and a 933-fold increase in efficiency. © 2025 IEEE.

关键词： Biselection consistent matrix (CM) fuzzy sets granular computing rough sets

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Perfect plane-wave source for a high-order symplectic finite-difference time-domain scheme

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Chinese Physics B 2011年第11期20卷 365-370页

作者：王辉黄志祥吴先良任信钢 Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education Anhui University Department of Physics and Electronic Engineering Hefei Normal University

The method of splitting a plane-wave finite-difference time-domain （SP-FDTD） algorithm is presented for the initiation of plane-wave source in the total-field / scattered-field （TF/SF） formulation of high-order symplectic finite- difference time-domain （SFDTD） scheme for the first time. By splitting the fields on one-dimensional grid and using the nature of numerical plane-wave in finite-difference time-domain （FDTD）, the identical dispersion relation can be obtained and proved between the one-dimensional and three-dimensional grids. An efficient plane-wave source is simulated on one-dimensional grid and a perfect match can be achieved for a plane-wave propagating at any angle forming an integer grid cell ratio. Numerical simulations show that the method is valid for SFDTD and the residual field in SF region is shrinked down to -300 dB.

关键词： splitting plane-wave finite-difference time-domain high-order symplectic finite-differencetime-domain scheme plane-wave source

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LP-Gaussians: Learnable Parametric Gaussian Splatting for Efficient Dynamic Reconstruction of Single-View Scenes

LP-Gaussians: Learnable Parametric Gaussian Splatting for Ef...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shaoqi Wu Weixing Xie Youhong Peng Jinwen Li Jiawei Yao Junfeng Yao Center for Digital Media Computing School of Film School of Informatics Xiamen University China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the popularity of short video platforms, the number of single-view videos has increased significantly. Existing NeRF-based methods can reconstruct dynamic scenes in a single-view setting, but slow rendering speed and low rendering quality limit their practical applications. To address these challenges, we propose a fast single-view scenes reconstruction framework based on 3D Gaussian Splatting. Our method uses point clouds obtained with depth priors as the Gaussian initialization and introduces learnable parametric functions to model the time-dependent deformation of Gaussians. The explicit deformation modeling for Gaussians significantly reduces training and rendering time. Furthermore, to improve the rendering quality of challenging areas, we adopt an adaptive sampling strategy to densify Gaussians. For occlusion problems from single-view videos, we design a smooth loss function to restore the color of the occluded areas. Experimental results demonstrate that our method significantly reduces training time, enhances rendering quality, and accelerates rendering speed. Project page: https://***/LPGaussians.

关键词： Training Point cloud compression Deformable models Adaptation models Three-dimensional displays Deformation Streaming media signal processing Rendering (computer graphics) Speech processing

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Color holographic zoom system based on a liquid lens

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Chinese Optics Letters 2015年第7期13卷 96-100页

作者：王迪王琼华沈川周昕刘超 School of Electronics and Information Engineering Sichuan University Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui University

In this Letter, we propose a color holographic zoom system based on a liquid lens. We use the spatial multiplexing method to realize color reconstruction. By controlling the focal lengths of the liquid lens and the encoded digital lens on the spatial light modulator panel, we can change the magnification of the reconstructed image very quickly, without mechanical parts and keeping the output plane stationary.

关键词： Color Holography Light modulators Multiplexing

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Wide-band EM Scattering from Multiple Dielectric targets above a Dielectric Rough Sea Surface Based on Maehly Approximation 7

Wide-band EM Scattering from Multiple Dielectric targets abo...

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2017 7th IEEE International Symposium on Microwave, Antenna, Propagation, and EMC Technologies

作者： Ran Bao An Qi Wang Zhi Xiang Huang Key Laboratory of intelligent Computing & Signal Processing Anhui University

ISBN: (纸本)9781509059300;9781509059294

In this paper an efficient method is proposed to simulate the wide-band electromagnetic(EM)scattering from multiple dielectric targets above a dielectric PM rough sea surface,which is based on Chebyshev series and Maehly *** with the traditional frequency domain algorithms,such as method of moments(MoM),the proposed algorithm is efficiently in reducing the computational *** results show that the Maehly approximation and the repeatedly simulations obtained by the MoM have achieved an excellent agreement both in horizontal and vertical polarization.

关键词： Maehly approximation method of moments PM rough sea surface

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Fast Simulations of Electromagnetic Scattering from 1-D Rough Surface through Adaptively Modified Characteristic Basis Function Method 7

Fast Simulations of Electromagnetic Scattering from 1-D Roug...

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2017 7th IEEE International Symposium on Microwave, Antenna, Propagation, and EMC Technologies

作者： Jing Jing Wang An Qi Wang Zhi Xiang Huang Key Laboratory of intelligent Computing & Signal Processing Anhui University

ISBN: (纸本)9781509059300;9781509059294

In this paper,the Adaptively Modified Characteristic Basis Function Method(AMCBFM) is proposed to fast simulate the electromagnetic scattering from one-dimensional rough ***,the Primary Characteristic Basis Functions(PCBFs) arising from the self-interaction within the self-block are generated,and the coefficients of the basis function and the first current of each block are ***,the Secondary Characteristic Basis Functions(SCBFs) which account for the mutual coupling effects from the other distinct domains are obtained by using the *** higher SCBFs can be also derived through the same ***,a new precision method is applied to control the current error,which is used to determine the order of the higher order *** Comparing numerical simulations through the AMCBFM and the traditional Characteristic Basis Function Method(CBFM),the AMCBFM can guarantee the accuracy and void higher iteration,and the convergence performance is better than the CBFM in the lower order ***,by comparing numerical results of the AMCBFM and the method of moments(MoM),the AMCBFM can effectively reduce the size of the impedance matrix,and significantly reduce computing time.

关键词： Rough surface Electromagnetic scattering Adaptively Modified Characteristic Basis Function Method of moments

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LDG: Lightweight Deformable 3D Gaussians for Single-View Dynamic Scene Reconstruction

LDG: Lightweight Deformable 3D Gaussians for Single-View Dyn...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Youhong Peng Weixing Xie Jinwen Li Shaoqi Wu Zefeng Wang Bingbing Hu Junfeng Yao Center for Digital Media Computing School of Film School of Informatics Xiamen University China National Institute for Data Science in Health and Medicine Xiamen University China Ministry of Culture and Tourism Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent deformable 3D Gaussians methods achieve high-quality reconstruction and real-time rendering. However, they require multi-view information and are not applicable to single-view dynamic scenes captured from mobile phones. Additionally, the high-dimensional hidden layer of deformation MLP and the excessive number of Gaussian primitives and attributes impose significant storage pressure, greatly limiting their practical application. To address the issues, we propose a novel Lightweight Deformable 3D Gaussians teacher-student framework. Specifically, we initialize Gaussian primitives with an initialization strategy designed for single-view scenes, and then optimize the teacher model using color and depth information. For the trained teacher model, we distill deformation MLP, prune Gaussian primitives and Gaussian attributes, and finally obtain a student model with low storage and high efficiency. Public benchmark experiments demonstrate the effectiveness of our framework, showing a compression rate exceeding 4× while maintaining satisfactory rendering quality. Project page: https://***/ldg/.

关键词： Deformable models Training Three-dimensional displays Deformation Memory management Color Rendering (computer graphics) Real-time systems Mobile handsets Speech processing

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Stfi: Structure-Aware and Time-Frequency Domain Interaction Network for Deformable Medical Image Registration

SSRN

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SSRN 2025年

作者： Hu, Rui Wang, Nian Zhang, Yuxin Li, Teng Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei230601 China Advanced Imaging Research Center University of Texas Southwestern Medical Center 5323 Harry Hines Blvd Dallas75390 United States School of Cyber Science and Engineering Southeast University No.2 Southeast University Road Nanjing211189 China

Deformable image registration is crucial in medical image analysis. Recently, unsupervised methods using intensity matching have shown promising results without labeled data. However, these methods still face challenges: (1) They often encounter local optima when handling anatomical structures with significant variations in intensity distribution;(2) Time domain convolution in decoders leads to loss of high-frequency fine features, limiting the effectiveness of generating fine deformation vector fields. To address these issues, we propose a novel structure-aware and time-frequency domain interaction (STFI) network. STFI integrates a structure-aware frequency domain loss and a time-frequency domain interaction module. The former leverages principal structural information from the weighted phase spectrum to guide the registration process, thereby reducing the risk of local optima. The latter processes and amalgamates information from both the time and frequency domains, effectively preserving high-frequency details. The efficacy and adaptability of our approach have been demonstrated on two types of data, offering a new perspective on exploiting time-frequency domain information for medical image registration. © 2025, The Authors. All rights reserved.

关键词： Image registration

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