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arXiv 2021年

作者： Baumgartner, Michael Jäger, Paul F. Isensee, Fabian Maier-Hein, Klaus H. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Interactive Machine Learning Group German Cancer Research Center Germany HIP Applied Computer Vision Lab. German Cancer Research Center Germany Pattern Analysis and Learning Group Heidelberg University Hospital Germany

Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions often depend on rating of objects rather than e.g. pixels. For this task, the cumbersome and iterative process of method configuration constitutes a major research bottleneck. Recently, nnU-Net has tackled this challenge for the task of image segmentation with great success. Following nnU-Net’s agenda, in this work we systematize and automate the configuration process for medical object detection. The resulting self-configuring method, nnDetection, adapts itself without any manual intervention to arbitrary medical detection problems while achieving results en par with or superior to the state-of-the-art. We demonstrate the effectiveness of nnDetection on two public benchmarks, ADAM and LUNA16, and propose 11 further medical object detection tasks on public data sets for comprehensive method evaluation. Code is at https://***/MIC-DKFZ/nnDetection. Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition

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Design and implementation of a kind of EUPS for mobile service

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Journal of Information and Computational Science 2010年第11期7卷 2248-2257页

作者： Zhang, Xiaodan Zhang, Degan Qiao, Xiaodong Liang, Bing Peng, Hongtao Institute of Scientific and Technical Information of China Beijing 100038 China Tianjin Key Lab. of Intelligent Computing and Novel software Technology Tianjin University of Technology Tianjin 300191 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300191 China Beijing Petroleum Managers Training Institute Beijing 100096 China

In order to support mobile service of long-distance monitoring and controlling UPS based on Web, a kind of design and implementation solution of embedded UPS (EUPS) system is brought forward in this paper. The design and implementation of system is based on ATmega161 and RTL8019AS with TCP/IP protocol suite for communication. In the EUPS system, an embedded file system is designed and implemented which can store the data and index information on a serial EEPROM chip in structured way and communicate with MCU through I2C bus. By embedding the file system into UPS system or other information appliances, users can browse and manipulate local data on the web client side. Based on our experimental tests, the performance of EUPS can meet the requirements of Web-based mobile services. © 2010 Binary Information Press.

关键词： Websites

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Adaptive feature selection for no-reference image quality assessment by mitigating semantic noise sensitivity 24

Adaptive feature selection for no-reference image quality as...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xudong Li Timin Gao Runze Hu Yan Zhang Shengchuan Zhang Xiawu Zheng Jingyuan Zheng Yunhang Shen Ke Li Yutao Liu Pingyang Dai Rongrong Ji Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University P.R. China School of Information and Electronics Beijing Institute of Technology Beijing China School of Medicine Xiamen University Tencent Youtu Lab. School of Computer Science and Technology Ocean University of China

The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods typically rely on feature extraction from upstream semantic backbone networks, assuming that all extracted features are relevant. However, we make a key observation that not all features are beneficial, and some may even be harmful, necessitating careful selection. Empirically, we find that many image pairs with small feature spatial distances can have vastly different quality scores, indicating that the extracted features may contain quality-irrelevant noise. To address this issue, we propose a Quality-Aware Feature Matching IQA Metric (QFM-IQM) that employs an adversarial perspective to remove harmful semantic noise features from the upstream task. Specifically, QFM-IQM enhances the semantic noise distinguish capabilities by matching image pairs with similar quality scores but varying semantic features as adversarial semantic noise and adaptively adjusting the upstream task's features by reducing sensitivity to adversarial noise perturbation. Furthermore, we utilize a distillation framework to expand the dataset and improve the model's generalization ability. Extensive experiments conducted on eight standard IQA datasets have demonstrated the effectiveness of our proposed QFM-IQM.

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Integrating global context contrast and local sensitivity for blind image quality assessment 24

Integrating global context contrast and local sensitivity fo...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xudong Li Runze Hu Jingyuan Zheng Yan Zhang Shengchuan Zhang Xiawu Zheng Ke Li Yunhang Shen Yutao Liu Pingyang Dai Rongrong Ji Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University P.R. China School of Information and Electronics Beijing Institute of Technology Beijing China School of Medicine Xiamen University. Tencent Youtu Lab. School of Computer Science and Technology Ocean University of China

Blind Image Quality Assessment (BIQA) mirrors subjective made by human observers. Generally, humans favor comparing relative qualities over predicting absolute qualities directly. However, current BIQA models focus on mining the "local" context, i.e., the relationship between information among individual images and the absolute quality of the image, ignoring the "global" context of the relative quality contrast among different images in the training data. In this paper, we present the Perceptual Context and Sensitivity BIQA (CSIQA), a novel contrastive learning paradigm that seamlessly integrates "global" and "local" perspectives into the BIQA. Specifically, the CSIQA comprises two primary components: 1) A Quality Context Contrastive Learning module, which is equipped with different contrastive learning strategies to effectively capture potential quality correlations in the global context of the dataset. 2) A Quality-aware Mask Attention Module, which employs the random mask to ensure the consistency with visual local sensitivity, thereby improving the model's perception of local distortions. Extensive experiments on eight standard BIQA datasets demonstrate the superior performance to the state-of-the-art BIQA methods.

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WIDER face and pedestrian challenge 2018 methods and results

arXiv

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arXiv 2019年

作者： Loy, Chen Change Lin, Dahua Ouyang, Wanli Xiong, Yuanjun Yang, Shuo Huang, Qingqiu Zhou, Dongzhan Xia, Wei Li, Quanquan Luo, Ping Yan, Junjie Wang, Jianfeng Li, Zuoxin Yuan, Ye Li, Boxun Shao, Shuai Yu, Gang Wei, Fangyun Ming, Xiang Chen, Dong Zhang, Shifeng Chi, Cheng Lei, Zhen Li, Stan Z. Zhang, Hongkai Ma, Bingpeng Chang, Hong Shan, Shiguang Chen, Xilin Liu, Wu Zhou, Boyan Li, Huaxiong Cheng, Peng Mei, Tao Kukharenko, Artem Vasenin, Artem Sergievskiy, Nikolay Yang, Hua Li, Liangqi Xu, Qiling Hong, Yuan Chen, Lin Sun, Mingjun Mao, Yirong Luo, Shiying Li, Yongjun Wang, Ruiping Xie, Qiaokang Wu, Ziyang Lu, Lei Liu, Yiheng Zhou, Wengang Nanyang Technological University Singapore Singapore Chinese University of Hong Kong Amazon Web Services University of Sydney SenseTime Megvii Microsoft Research Asia University of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Jd Ai Research. Computer Vision and Multimedia Lab NtechLab Shanghai Jiao Tong University University of Science and Technology of China and Iflytek

This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian. The challenge focuses on the problem of precise localization of human faces and bodies, and accurate association of identities. It comprises of three tracks: (i) WIDER Face which aims at soliciting new approaches to advance the state-of-the-art in face detection, (ii) WIDER Pedestrian which aims to find effective and efficient approaches to address the problem of pedestrian detection in unconstrained environments, and (iii) WIDER Person Search which presents an exciting challenge of searching persons across 192 movies. In total, 73 teams made valid submissions to the challenge tracks. We summarize the winning solutions for all three tracks. and present discussions on open problems and potential research directions in these topics. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

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Research of deep packet inspection system based on the MapReduce

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Journal of Computational Information Systems 2013年第7期9卷 2587-2594页

作者： Wang, Jinsong Wei, Jun Lin, Sheng Huang, Wei Tianjin Key Lab. of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300384 China

In this paper, we propose a deep packet inspection system based on the MapReduce. The MapReduce which is a parallel distributed programming model developed by Google applies the technology of deep packet inspection. Network protocol analysis technique is invoked to realize detect, effectively reducing the packet number to be matched. Furthermore, cache technology is adopted to store attack signature from database in order to reduce the frequency of reading the rules from library file which improved the detection efficiency. Experimental results show that effectiveness of the proposed system with the high-speed network traffic. Copyright © 2013 Binary Information Press.

关键词： MapReduce

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Abnormal network traffic detection approach based on alive entropy

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Tongxin Xuebao/Journal on Communications 2013年第SUPPL.2期34卷 51-57页

作者： Mu, Xiang-Kun Wang, Jin-Song Xue, Yu-Feng Huang, Wei Tianjin Key Lab. of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300384 China

A novel alive entropy-based detection approach was proposed, which detects the abnormal network traffic based on the values of alive entropies. The alive entropies calculated based on the NetFlow data coming from the network traffic of input and output of a whole system, which is essentially a monitored network. In order to decrease false positive rate of abnormal network traffic, different scales are selected to compute the values of alive entropies in different sizes of network traffic. With the low false positive rate of abnormal network traffic, the abnormal network traffic can be effectively detected. Experiments carried out on a real campus network were used to evaluate the effectiveness of the proposed approach. A comparative study illustrates that the proposed approach may easily detect the abnormal network traffic with random characteristics in comparison with some "conventional" approaches reported in the literatures.

关键词： Entropy

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Research on adaptive strategy for end-hopping system

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Tongxin Xuebao/Journal on Communications 2011年第11 A期32卷 51-57页

作者： Zhao, Chun-Lei Jia, Chun-Fu Weng, Chen Lin, Kai College of Information Technical Science Nankai University Tianjin 300071 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300384 China Tianjin Key Lab. of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China

An adaptive strategy was proposed for EH system. Modules of attack detection, feedback transmission and adaptive control were added to original EH system. They could help to make threats analysis and adjust hopping strategy in real time, so as to reduce the probability of the system stepping into the attackers' target area. With the experiments proving the anti-attack ability of the prototype system, the feasibility and effectiveness of adaptive strategy were demonstrated for EH system.

关键词： Network security

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NTIRE 2020 Challenge on Image and Video Deblurring

arXiv

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arXiv 2020年

作者： Seungjun, Nah Sanghyun, Son Radu, Timofte Kyoung Mu, Lee Tseng, Yu Xu, Yu-Syuan Chiang, Cheng-Ming Tsai, Yi-Min Brehm, Stephan Scherer, Sebastian Xu, Dejia Chu, Yihao Sun, Qingyan Jiang, Jiaqin Duan, Lunhao Yao, Jian Purohit, Kuldeep Suin, Maitreya Rajagopalan, A.N. Ito, Yuichi Hrishikesh, P.S. Puthussery, Densen Akhil, K.A. Jiji, C.V. Kim, Guisik Deepa, P.L. Xiong, Zhiwei Huang, Jie Liu, Dong Kim, Sangmin Nam, Hyungjoon Kim, Jisu Jeong, Jechang Huang, Shihua Fan, Yuchen Yu, Jiahui Yu, Haichao Huang, Thomas S. Zhou, Ya Li, Xin Liu, Sen Chen, Zhibo Dutta, Saikat Das, Sourya Dipta Garg, Shivam Sprague, Daniel Patel, Bhrij Huck, Thomas Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland MediaTek Inc University of Augsburg Chair for Multimedia Computing and Computer Vision Lab Germany Peking University China Beijing University of Posts and Telecommunications China Beijing Jiaotong University China Wuhan University China Indian Institute of Technology Madras India Vermilion College of Engineering Trivandrum India CVML Chung-Ang University Korea Republic of APJ Abdul Kalam Technological University India University of Science and Technology of China China Image Communication Signal Processing Laboratory Hanyang University Korea Republic of Southern University of Science and Technology China University of Illinois at Urbana-Champaign United States CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China IIT Madra Jadavpur University India University of Texas Austin United States Duke University Computer Science Department United States

Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this challenge, we present the evaluation results from 3 competition tracks as well as the proposed solutions. Track 1 aims to develop single-image deblurring methods focusing on restoration quality. On Track 2, the image deblurring methods are executed on a mobile platform to find the balance of the running speed and the restoration accuracy. Track 3 targets developing video deblurring methods that exploit the temporal relation between input frames. In each competition, there were 163, 135, and 102 registered participants and in the final testing phase, 9, 4, and 7 teams competed. The winning methods demonstrate the state-of-the-art performance on image and video deblurring tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Image enhancement

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Journal of Information Science and Engineering: Editorial notice

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Journal of Information Science and Engineering 2005年第5期21卷 i-ii页

作者： Lee, Der-Tsai Amato, Nancy M. Chang, Shih-Fu Chen, Homer H. Chen, Tsuhan Hsu, Tsan-Sheng Hwang, Jenq-Neng Kuo, Sy-Yen Kuo, Tei-Wei Li, Chung-Sheng Tokuyama, Takeshi Wing, Jeannette Wu, Tzong-Chen Yen, John Computer Science at Texas A and M University United States Parasol Laboratory United States IEEE Transactions on Parallel and Distributed Systems Computing Research Association's Committee on the Status of Women in Computing Research CRA-W's Distributed Mentor Program United States Department of Electrical Engineering Columbia University United States Lab. United States College of Electrical Engineering and Computer Science National Taiwan University Taiwan IEEE Transactions on Circuits and Systems for Video Technology IEEE Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States ACM ACM SIGACT IEEE Computer Society IICM Austria Research and Development of the Department Multimedia Signal Processing Technical Committee IEEE Signal Processing Society United States IEEE Transactions on Circuits and systems for Video Technology College of Electrical Engineering and Computer Science National Taiwan Ocean University Keelung Taiwan Department of Electrical Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan IEEE Technical Committee on Real-Time Systems Computer Science Division IBM T.J. Watson Research Center United States IBM Research Division Graduate School of Information Sciences Tohoku University Japan ACM IPSJ Mathematical Society of Japan Japan Department of Computer Science Computer Science Department Carnegie Mellon University United States National Academies of Science's Computer Science and Telecommunications Board United States Microsoft's Trustworthy Academic Advisory Board Intel Research Pittsburgh's Advisory Board United States Dartmouth's Institute for Security Technology Studies Advisory Committee Canada Sloan Research Fellowships Program Committee United States ACM Taiwan China Information Sciences and Technology Pennsylvania State University United States Laboratory for Intelligent Agents Penn State's School of Info

No abstract availab.e

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