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检索条件"机构=Image Processing & Pattern Recognition Laboratory"
519 条 记 录,以下是481-490 订阅
排序:
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 method... 详细信息
来源: 评论
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, wh... 详细信息
来源: 评论
Sparse Kernel Regression with Coefficient-based `q−regularization
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Journal of Machine Learning Research 2019年 20卷
作者: Shi, Lei Huang, Xiaolin Feng, Yunlong Suykens, Johan A.K. Shanghai Key Laboratory for Contemporary Applied Mathematics School of Mathematical Sciences Fudan University Shanghai China Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing Shanghai China Department of Mathematics and Statistics State University of New York at Albany New York United States Department of Electrical Engineering ESAT-STADIUS KU Leuven Kasteelpark Arenberg 10 LeuvenB-3001 Belgium
In this paper, we consider the `q−regularized kernel regression with 0 q−penalty term over a linear span of features generated by a kernel function. We study the asymptotic behavior of the algorithm under the framewor... 详细信息
来源: 评论
SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical image Segmentation
SliceProp: A Slice-Wise Bidirectional Propagation Model for ...
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Medical Artificial Intelligence (MedAI), IEEE International Conference on
作者: Xin Xu Wenjing Lu Jiahao Lei Peng Qiu Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China Department of Vascular Surgery Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China
Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining ...
来源: 评论
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. Requi... 详细信息
来源: 评论
Efficient spatialtemporal context modeling for action recognition?
arXiv
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arXiv 2021年
作者: Cao, Congqi Lu, Yue Zhang, Yifan Jiang, Dongmei Zhang, Yanning Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Lab on Speech and Image Information Processing School of Computer Science Northwestern Poly-technical University Xi'an710129 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100190 China
Contextual information plays an important role in action recognition. Local operations have difficulty to model the relation between two elements with a long-distance interval. However, directly modeling the contextua... 详细信息
来源: 评论
3D EAGAN: 3D edge-aware attention generative adversarial network for prostate segmentation in transrectal ultrasound images
arXiv
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arXiv 2023年
作者: Liu, Mengqing Shao, Xiao Jiang, Liping Wu, Kaizhi School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Computer Science Nanjing University of Information Science and Technology Jiangsu Nanjing China The First Affiliated Hospital of Nanchang University Nanchang University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China
Background: Segment prostates from transrectal ultrasound (TRUS) images plays an essential role in the diagnosis and treatment of prostate cancer. However, traditional segmentation methods are time-consuming and labor... 详细信息
来源: 评论
Novel low power supply DC-coupled 1.25 Gb/s laser diode driver
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Guti Dianzixue Yanjiu Yu Jinzhan/Research and Progress of Solid State Electronics 2006年 第4期26卷 498-503页
作者: Fu, Shengmeng Chen, Zhaoyang Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory for Image Information Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China
Since the DC-coupled interface between the driver and the laser diode makes it impossible for the conventional drivers to work with low power supply, an output stage has been proposed. A novel APC can suppress the out... 详细信息
来源: 评论
Boosting discriminative model for moving cast shadow detection
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Jisuanji Xuebao/Chinese Journal of Computers 2007年 第8期30卷 1295-1301页
作者: Zha, Yu-Fei Chu, Ying Wang, Xun Ma, Shi-Ping Bi, Du-Yan Signal and Information Processing Laboratory Engineering College Air Force Engineering University Xi'an 710038 China Key Laboratory for Image Processing and Intelligent Control Institute of Pattern Recognition and Artificial Intelligence Huazhong Univ. of Sci. and Technol. Wuhan 430074 China
Moving cast shadow causes serious problem while segmenting and extracting foreground from image sequences, due to the misclassification of moving shadow as foreground. This paper proposes a Boosting discriminative mod... 详细信息
来源: 评论
Sparse generalized canonical correlation analysis: Distributed alternating iteration based approach
arXiv
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arXiv 2020年
作者: Cai, Jia Lv, Kexin Huo, Junyi Huang, Xiaolin Yang, Jie School of Statistics and Mathematics Guangdong University of Finance & Economics Big Data and Educational Statistics Application Laboratory 21 Chisha Road Guangzhou Guangdong510320 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China School of Electronics and Computer Science University of Southampton University Road SouthamptonSO17 1BJ United Kingdom
Sparse canonical correlation analysis (CCA) is a useful statistical tool to detect latent information with sparse structures. However, sparse CCA works only for two datasets, i.e., there are only two views or two dist... 详细信息
来源: 评论