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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), International Conference on

作者： Guiwen Zhang Qishen Li Qiufeng Li Taida Wu School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

to improve the clarity of objects in a dark-light environment, and to facilitate the identification and detection of targets behind. People perceive the color and brightness of a point not only depending on the pixel value of the point but also the absolute light entering the human eye is related to the color and brightness around the point. The self-calibration model we used considers the surrounding information, which avoids the information pollution, such as large regions of dark background information. In this model, we introduce a heterogeneous convolution filter, which makes reasonable use of different parts of the filter. Through this operation, information from multiple different scale spaces can be fused, and the field of view when applying the convolution layer is greatly increased without increasing the hyper-parameters, thus producing a more distinctive feature representation. Then the self-calibration model is combined with the backbone reinforcement network, which can not only retain the information of the original scale space but also efficiently collect the latent space information to guide the feature transformation in the original space. After the different channels of the image are processed separately, the dependency between the channels can be established by using the heterogeneous convolution filter. Finally, the test on the ExDark data set proves that our dark target enhancement effect has been significantly improved.

关键词： Training Pollution image color analysis Convolution Brightness Lighting Information filters

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Modeling Inter-Intra Heterogeneity for Graph Federated Learning

arXiv

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arXiv 2024年

作者： Yu, Wentao Chen, Shuo Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Center for Advanced Intelligence Project RIKEN Japan State Key Laboratory of Complex & Critical Software Environment Beihang University China Jinan University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e., subgraphs). However, their inter-subgraph similarities estimated with the outputs of local models are less reliable, because the final outputs of local models may not comprehensively represent the real distribution of subgraph data. In addition, they ignore the critical intra-heterogeneity which usually exists within each subgraph itself. To address these issues, we propose a novel Federated learning method by integrally modeling the Inter-Intra Heterogeneity (FedIIH). For the inter-subgraph relationship, we propose a novel hierarchical variational model to infer the whole distribution of subgraph data in a multi-level form, so that we can accurately characterize the inter-subgraph similarities with the global perspective. For the intra-heterogeneity, we disentangle the subgraph into multiple latent factors and partition the model parameters into multiple parts, where each part corresponds to a single latent factor. Our FedIIH not only properly computes the distribution similarities between subgraphs, but also learns disentangled representations that are robust to irrelevant factors within subgraphs, so that it successfully considers the inter- and intra- heterogeneity simultaneously. Extensive experiments on six homophilic and five heterophilic graph datasets in both non-overlapping and overlapping settings demonstrate the effectiveness of our method when compared with nine state-of-the-art methods. Specifically, FedIIH averagely outperforms the second-best method by a large margin of 5.79% on all heterophilic datasets. Code is available at https://***/blgpb/FedIIH. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Remote Sensing image Object Detection Method with Feature Denoising Fusion Module

Remote Sensing Image Object Detection Method with Feature De...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Penghui Chen Qishen Li Qiufeng Li Zhongyu Wu School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

Remote sensing object detection is an important research area in computer vision, widely applied in both military and civilian domains. However, challenges in remote sensing image object detection such as large image sizes, complex backgrounds, and significant variations in target scales are prevalent. To address these issues, this paper proposes a new Feature Denoising and Fusion Module (FDFM) aimed at enhancing the accuracy and robustness of object detection. This module comprises a Multi-Scale Denoising Submodule(MDS) and an Attention Optimization Submodule(AOS). The Multi-Scale Denoising Module aims to suppress lower-level texture noise by utilizing higher-level semantic features before the fusion process, reducing the impact of lower-level noise on subsequent multi-scale feature fusion. Meanwhile, the Attention Optimization Module seeks to enhance the precision of self-attention computations within the Multi-Scale Denoising Module without increasing the parameter count. The efficacy of this method was evaluated on public datasets DOTA, VisDrone, VOC and COCO, showing improvements in comparison to baseline models.

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Edge-Aware Graph Attention Network for Ratio of Edge-User Estimation in Mobile Networks

Edge-Aware Graph Attention Network for Ratio of Edge-User Es...

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International Conference on pattern recognition

作者： Jiehui Deng Sheng Wan Xiang Wang Enmei Tu Xiaolin Huang Jie Yang Chen Gong PCA Lab the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Hong Kong Polytechnic University Hong Kong SAR China

Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, which are experience-dependent and labor-intensive, and thus the estimated REU might be imprecise. Considering the inherited graph structure of mobile networks, in this paper, we utilize a graph-based deep learning method for automatic REU estimation, where the practical cells are deemed as nodes and the load switchings among them constitute edges. Concretely, Graph Attention Network (GAT) is employed as the backbone of our method due to its impressive generalizability in dealing with networked data. Nevertheless, conventional GAT cannot make full use of the information in mobile networks, since it only incorporates node features to infer the pairwise importance and conduct graph convolutions, while the edge features that are actually critical in our problem are disregarded. To accommodate this issue, we propose an Edge-Aware Graph Attention Network (EAGAT), which is able to fuse the node features and edge features for REU estimation. Extensive experimental results on two real-world mobile network datasets demonstrate the superiority of our EAGAT approach to several state-of-the-art methods.

关键词： Deep learning Fuses Estimation Switches pattern recognition

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MambaMIM: Pre-training Mamba with State Space Token-interpolation

arXiv

引用

arXiv 2024年

作者： Tang, Fenghe Nian, Bingkun Li, Yingtai Yang, Jie Wei, Liu Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

Generative self-supervised learning demonstrates outstanding representation learning capabilities in both Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). However, there are currently no generative pre-training methods related to selective state space models (Mamba) that can handle long-range dependencies effectively. To address this challenge, we introduce a generative self-supervised learning method for Mamba (MambaMIM) based on Selective Structure State Space Sequence Token-interpolation (S6T), a general-purpose pre-training method for arbitrary Mamba architectures. Our method, MambaMIM, incorporates a bottom-up 3D hybrid masking strategy in the encoder to maintain masking consistency across different architectures. Additionally, S6T is employed to learn causal relationships between the masked sequence in the state space. MambaMIM can be used on any single or hybrid Mamba architectures to enhance the Mamba long-range representation capability. Extensive downstream experiments reveal the feasibility and advancement of using Mamba for pretraining medical image tasks. The code is available at: https://***/FengheTan9/MambaMIM. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Modified linear discriminant analysis and its application to face recognition

引用

Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University 2005年第4期39卷 527-530页

作者： Zhou, Da-Ke Yang, Xin Peng, Ning-Song Inst. of Image Processing and Pattern Recognition Shanghai Jiaotong Univ. Shanghai 200030 China Inst. of Electronic and Information Henan Univ. of Science and Technology Luoyang 471039 China

This paper presented an improved linear discriminant analysis (LDA) algorithm for face recognition, which can effectively deal with the two problems in traditional LDA-based approaches: (1) the small sample size problem, and (2) the Fisher criterion is nonoptimal with respect to classification rate. In particular, the proposed algorithm can also improve the classification rate of one or several appointed classes. The key to this method is to use the technique that it can reserves the significant discriminatory information for dimension reduction and meanwhile utilize a modified Fisher criterion. The comparative experiments on ORL face database verify the effectiveness of the proposed method.

关键词： Feature extraction

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New de-interlacing method based on adaptive weight

引用

Chinese Journal of Systems Engineering and Electronics 2004年第2期000卷

De-interlacing is very important when converting interlaced pictures to progressive pictures in format ***-formats digital broadcast and progressive display requires the de-interlacing technique. An adaptive weight deinterlacing method is proposed. It combines motion compensation technique with directional-based spatio-temporal filter efficiently. Experiment results indicate that the method can keep edge continuity and sharpness effectively, reduce the artifacts in motion areas, and shows better visual performance when the estimated motion vectors are inaccurate.

关键词： de-interlacing motion compensation spatio-temporal filter

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C64x-based multi-DSP real-time image processing system

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Jisuanji Gongcheng/Computer Engineering 2006年第11期32卷 268-270页

作者： Hu, Junhong Jiang, Haoyang Fan, Rong Zhang, Tianxu Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

A C64x-based multi-DSP real-time image processing system is introduced, which uses high performance TMS320C6414 DSP to process image and FPGA device to realize LINK port to transport image data with LVDS signal. Requirements of image processing performance and image data communication of image fusion are met. Based on the hardware system, a real time microkernel based distributed operating system is designed and implemented. At the end, its real-time performance is analyzed from three aspects. It's shown that the real time image processing system can reach the requirements of real time image processing.

关键词： FPGA Multi-DSP system Real-time image processing

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Optimal decoupling control system using kernel method

引用

Chinese Journal of Systems Engineering and Electronics 2004年第3期000卷

A major difficulty in multivariable control design is the cross-coupling between inputs and outputs which obscures the effects of a specific controller on the overall behavior of the system. This paper considers the application of ker nel method in decoupling multivariable outputfeedback controllers. Simulation results are presented to show the feasibility of the proposed technique.

关键词： support vector regression kernel ridge regression decoupling multivariable control systems

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A 3.84 GIPS integrated memory array processor

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SYSTEMS AND COMPUTERS IN JAPAN 1996年第3期27卷 26-36页

作者： Fujita, Y Yamashita, N Kimura, T Nakamura, K Okazaki, S Nonmember NEC Corporation Kawasaki Japan 216 Member Shin'ichi Okazaki received his B.E. and M.E. degrees from Osaka University in 1982 and 1984 respectively. He is currently with the Pattern Recognition Research Division of NEC Information Media Laboratories. His research interest is high-speed image processing. He was a Visiting Researcher at Stanford University from 1991 to 1992.

An integrated memory array processor (IMAP) ULSI with 64 processing elements and a 2 Mb SRAM has been developed to build a compact real-time image processing system. The chip attains a 3.84 GIPS peak performance through the use of SIMD parallel processing and 1.28 GByte/s on-chip processor-memory bandwidth. The IMAP is capable of parallel indexed addressing, which increases applications for parallel algorithms. Created using 0.55 mu m BiCMOS double layer metal process technology, the IMAP contains 11 million translators in a 15.1 x 15.6 mm(2) die area.

关键词： parallel processing multiple processors SIMD VLSI image processing

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