检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Phipps, Kaleb Lerch, Sebastian Andersson, Maria Mikut, Ralf Hagenmeyer, Veit Ludwig, Nicole Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Department of Economics Karlsruhe Institute of Technology Germany Department of Management and Engineering Division of Energy Systems Linköping University Sweden Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Excellence Machine Learning University of Tübingen Germany

Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather system, they can be used to propagate this uncertainty through to subsequent wind power forecasting models. However, as weather ensemble systems are known to be biased and underdispersed, meteorologists post-process the ensembles. This postprocessing can successfully correct the biases in the weather variables but has not been evaluated thoroughly in the context of subsequent forecasts, such as wind power generation forecasts. Copyright © 2020, The Authors. All rights reserved.

关键词： Electric power generation

来源：评论

学校读者我要写书评

暂无评论

A Survey of the Self Supervised learning Mechanisms for Vision Transformers

arXiv

引用

arXiv 2024年

作者： Khan, Asifullah Sohail, Anabia Fiaz, Mustansar Hassan, Mehdi Afridi, Tariq Habib Marwat, Sibghat Ullah Munir, Farzeen Ali, Safdar Naseem, Hannan Zaheer, Muhammad Zaigham Ali, Kamran Sultana, Tangina Tanoli, Ziaurrehman Akhter, Naeem Pattern Recognition Lab DCIS PIEAS Nilore Islamabad45650 Pakistan PIEAS Nilore Islamabad45650 Pakistan Deep Learning Lab Center for Mathematical Sciences PIEAS Nilore Islamabad45650 Pakistan Center of Secure Cyber-Physical Security Systems Khalifa University Abu Dhabi United Arab Emirates IBM Research United States Department of Computer Science Air University Islamabad Pakistan Department of Computer Science and Engineering Kyung Hee University Global Campus 1732 Gyeonggi-do Yongin17104 Korea Republic of Department of Electrical Engineering and Automation Aalto University Finland Finnish Center of Artificial Center Finland Faculty of Engineering and Green Technology Universiti Tunku Abdul Rahman Malaysia Computer Vision Department Mohamed Bin Zayed University of Artificial Intelligence United Arab Emirates Karachi Pakistan Department of Electronics and Communication Engineering Hajee Mohammad Danesh Science and Technology University Bangladesh HiLIFE University of Helsinki Finland

Vision Transformers (ViTs) have recently demonstrated remarkable performance in computer vision tasks. However, their parameter-intensive nature and reliance on large amounts of data for effective performance have shifted the focus from traditional human-annotated labels to unsupervised learning and pretraining strategies that uncover hidden structures within the data. In response to this challenge, self-supervised learning (SSL) has emerged as a promising paradigm. SSL leverages inherent relationships within the data itself as a form of supervision, eliminating the need for manual labeling and offering a more scalable and resource-efficient alternative for model training. Given these advantages, it is imperative to explore the integration of SSL techniques with ViTs, particularly in scenarios with limited labeled data. Inspired by this evolving trend, this survey aims to systematically review SSL mechanisms tailored for ViTs. We propose a comprehensive taxonomy to classify SSL techniques based on their representations and pre-training tasks. Additionally, we discuss the motivations behind SSL, review prominent pre-training tasks, and highlight advancements and challenges in this field. Furthermore, we conduct a comparative analysis of various SSL methods designed for ViTs, evaluating their strengths, limitations, and applicability to different scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

arXiv

引用

arXiv 2022年

作者： Pérez-Pellitero, Eduardo Catley-Chandar, Sibi Shaw, Richard Leonardis, Aleš Timofte, Radu Zhang, Zexin Liu, Cen Peng, Yunbo Lin, Yue Yu, Gaocheng Zhang, Jin Ma, Zhe Wang, Hongbin Chen, Xiangyu Wang, Xintao Wu, Haiwei Liu, Lin Dong, Chao Zhou, Jiantao Yan, Qingsen Zhang, Song Chen, Weiye Liu, Yuhang Zhang, Zhen Zhang, Yanning Shi, Javen Qinfeng Gong, Dong Zhu, Dan Mengdi Sun Chen, Guannan Hu, Yang Li, Haowei Zou, Baozhu Liu, Zhen Lin, Wenjie Jiang, Ting Jiang, Chengzhi Li, Xinpeng Han, Mingyan Fan, Haoqiang Sun, Jian Liu, Shuaicheng Marín-Vega, Juan Sloth, Michael Schneider-Kamp, Peter Röttger, Richard Li, Chunyang Bao, Long He, Gang Xu, Ziyao Xu, Li Zhan, Gen Sun, Ming Wen, Xing Li, Junlin Li, Jinjing Li, Chenghua Gang, Ruipeng Liu, Chenming Feng, Shuang Lei, Fei Liu, Rui Ruan, Junxiang Dai, Tianhong Li, Wei Lu, Zhan Liu, Hengyan Huang, Peian Ren, Guangyu Luo, Yonglin Liu, Chang Tu, Qiang Li, Fangya Ma, Sai Cao, Yizhen Tel, Steven Heyrman, Barthelemy Ginhac, Dominique Lee, Chul Kim, Gahyeon Park, Seonghyun Vien, An Gia Mai, Truong Thanh Nhat Yoon, Howoon Vo, Tu Holston, Alexander Zaheer, Sheir Park, Chan Y. Huawei Noah's Ark Laboratory China University of Würzburg Eth Zürich Switzerland Netease Games Ai Lab China AntGroup China University of Macau China Shenzhen Institutes of Advanced Technology Cas China Tencent Pcg China University of Science and Technology of China China The University of Adelaide Australia Xidian University China Northwestern Polytechnical University China The University of New South Wales Australia Boe Technology Group Co. Ltd. China Czur Technology Group Co. Ltd. China Megvii Technology China University of Southern Denmark Denmark Esoft Systems Xiaomi ByteDance Kuaishou Technology China Communication University of China China Institute of Automation Chinese Academy of Sciences China Academy of Broadcasting Science Nrta United States Tetras Ai Technology Imperial College London United Kingdom Tsinghua University China The University of Edinburgh United Kingdom Sun Yat-sen University China Shanghai Jiao Tong University China Beijing Institute of Technology China ImViA Laboratory University of Burgundy France Department of Multimedia Engineering Dongguk University Korea Republic of Gachon University Korea Republic of Kc Machine Learning Lab Korea Republic of

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) observations, which might suffer from under- or over-exposed regions and different sources of noise. The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i.e. solutions can not exceed a given number of operations). In Track 2, participants are asked to minimize the complexity of their solutions while imposing a constraint on fidelity scores (i.e. solutions are required to obtain a higher fidelity score than the prescribed baseline). Both tracks use the same data and metrics: Fidelity is measured by means of PSNR with respect to a ground-truth HDR image (computed both directly and with a canonical tonemapping operation), while complexity metrics include the number of Multiply-Accumulate (MAC) operations and runtime (in seconds). Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Evolution and Neural Representation of Mammalian Cooperative Behavior

SSRN

引用

SSRN 2021年

作者： Jiang, Mengping Wang, Miaoyaoxin Shi, Qianqian Wei, Lei Lin, Yongqin Wu, Dingcheng Liu, Boyi Nie, Xiupeng Qiao, Hong Xu, Lin Yang, Tianming Wang, Zuoren Institute of Neuroscience State Key Laboratory of Neuroscience CAS Center for Excellence in Brain Science &Intelligence Technology Chinese Academy of Sciences Shanghai200031 China Key Laboratory of Animal Models and Human Disease Mechanisms and Laboratory of Learning and Memory Kunming Institute of Zoology Chinese Academy of Sciences KunmingYunnan650223 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Cooperation is common in nature and has been pivotal to the development of human society. However, the details of how and why cooperation evolved remain poorly understood. Cross-species investigation of cooperation may help to elucidate the evolution of cooperative strategies. Thus, we designed a novel cooperative behavioral paradigm and quantitatively examined the cooperative abilities and strategies of mice, rats, and tree shrews. We found that social communication played a key role in the establishment of new cooperation, and that increased cooperative ability and a more efficient cooperative strategy emerged as a function of the evolutionary hierarchy of the tested species. We also found neural representations of distinct aspects of cooperation in the brains of rats through in vivo electrophysiological recording. Cooperation-preferential signals were identified in addition to material reward-preferential signals, each of which may represent internal drives for cooperation. © 2021, The Authors. All rights reserved.

关键词： Rats

来源：评论

学校读者我要写书评

暂无评论

An Early Stage Researcher's Primer on systems Medicine Terminology

Network and Systems Medicine

引用

Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Controllability limit of edge dynamics in complex networks

引用

Physical Review E 2019年第2期100卷 022318-022318页

作者： Shao-Peng Pang Wen-Xu Wang Fei Hao School of Electrical Engineering and Automation Qilu University of Technology (Shandong Academy of Science) Jinan Shandong Province 250353 China State Key Laboratory of Cognitive Neuroscience and Learning IDG/McGovern Institute for Brain & Research Beijing Normal University Beijing 100875 China School of Systems Science Beijing Normal University Beijing 100875 China The Seventh Research Division School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China

Edge dynamics is relevant to various real-world systems with complex network topological features. An edge dynamical system is controllable if it can be driven from any initial state to any desired state in finite time with appropriate control inputs. Here a framework is proposed to study the impact of correlation between in- and out-degrees on controlling the edge dynamics in complex networks. We use the maximum matching and direct acquisition methods to determine the controllability limit, i.e., the limit of acceptable change of the edge controllability by adjusting the degree correlation only. Applying the framework to plenty complex networks, we find that the controllability limits are ubiquitous in model and real networks. Arbitrary edge controllability in between the limits can be achieved by properly adjusting the degree correlation. Moreover, a nonsmooth phenomenon occurs in the upper limits, and exponential and power-law scaling behaviors are widespread in the approach or separation speed between the upper and lower limits.

关键词： Degree correlations Degree distributions Dynamics of networks Barabasi-Albert network Erdos-Renyi graph Watts-Strogatz network

来源：评论

学校读者我要写书评

暂无评论

Self-Awareness for Autonomous systems

引用

PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

来源：评论

学校读者我要写书评

暂无评论

Understanding metric-related pitfalls in image analysis validation

arXiv

引用

arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial for Special Issue on Autonomous Control of Unmanned Aerial Vehicles

引用

IEEE/CAA Journal of Automatica Sinica 2015年第1期2卷 1-1页

作者： Derong Liu Changyin Sun Bin Xian the IEEE Computational Intelligence Society the IEEE Transactions on Neural Networks and Learning Systems 2016 World Congress on Intelligent Control and Automation Southeast University of China University of Science and Technology Beijing School of Electrical Engineering and Automation at Tianjin University

IN recent years,unmanned aerial vehicles(UAVs)have been widely employed in different applications,both military and ***,a fast growing civil UAV market is predicted over the next ***,most currently developed UAVs depend on simple control *** require exact modeling of the UAVs dynamics and are vulnerable to external ***,there is great

关键词： for in et is UAV Guest Editorial for Special Issue on Autonomous Control of Unmanned Aerial Vehicles on of

来源：评论

学校读者我要写书评

暂无评论

Depth of field rendering via adaptive recursive filtering 14

Depth of field rendering via adaptive recursive filtering

引用

SIGGRAPH Asia 2014 Technical Briefs, SA 2014

作者： Xu, Shibiao Mei, Xing Dong, Weiming Sun, Xun Shen, Xukun Zhang, Xiaopeng NLPR Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Baidu Institute of Deep Learning Beijing China

ISBN: (纸本)9781450328951

We present a new post-processing method for rendering high-quality depth-of-field effects in real time. Our method is based on a recursive filtering process, which adaptively smooths the image frame with local depth and circle of confusion information. Unlike previous post-filtering approaches that rely on various convolution kernels, the behavior of our filter is controlled by a weighting function defined between two neighboring pixels. By properly designing this weighting function, our method produces spatially-varying smoothed results, correctly handles the boundaries between in-focus and out-of-focus objects, and avoids rendering artifacts such as intensity leakage and blurring discontinuity. Additionally, our method works on the full frame without resorting to image pyramids. Our algorithms runs efficiently on graphics hardware. We demonstrate the effectiveness of the proposed method with several complex scenes.

关键词： Information filtering

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：