检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Maier-Hein, Lena Eisenmann, Matthias Sarikaya, Duygu März, Keno Collins, Toby Malpani, Anand Fallert, Johannes Feussner, Hubertus Giannarou, Stamatia Mascagni, Pietro Nakawala, Hirenkumar Park, Adrian Pugh, Carla Stoyanov, Danail Vedula, Swaroop S. Cleary, Kevin Fichtinger, Gabor Forestier, Germain Gibaud, Bernard Grantcharov, Teodor Hashizume, Makoto Heckmann-Nötzel, Doreen Kenngott, Hannes G. Kikinis, Ron Mündermann, Lars Navab, Nassir Onogur, Sinan Sznitman, Raphael Taylor, Russell H. Tizabi, Minu D. Wagner, Martin Hager, Gregory D. Neumuth, Thomas Padoy, Nicolas Collins, Justin Gockel, Ines Goedeke, Jan Hashimoto, Daniel A. Joyeux, Luc Lam, Kyle Leff, Daniel R. Madani, Amin Marcus, Hani J. Meireles, Ozanan Seitel, Alexander Teber, Dogu Ückert, Frank Müller-Stich, Beat P. Jannin, Pierre Speidel, Stefanie Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Medical Faculty Heidelberg University Heidelberg Germany Department of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey LTSI Inserm UMR 1099 University of Rennes 1 Rennes France IRCAD Strasbourg France The Malone Center for Engineering in Healthcare The Johns Hopkins University BaltimoreMD United States KARL STORZ SE and Co. KG Tuttlingen Germany Department of Surgery Klinikum Rechts der Isar Technical University of Munich Munich Germany The Hamlyn Centre for Robotic Surgery Imperial College London London United Kingdom ICube University of Strasbourg CNRS France IHU Strasbourg Strasbourg France Altair Robotics Lab University of Verona Verona Italy Department of Surgery Anne Arundel Health System AnnapolisMD United States Johns Hopkins University School of Medicine BaltimoreMD United States Department of Surgery Stanford University School of Medicine StanfordCA United States Wellcome/EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany The Sheikh Zayed Institute for Pediatric Surgical Innovation Children's National Hospital WashingtonDC United States The Perk Lab Queen's University KingstonON Canada University of Haute-Alsace Mulhouse France Faculty of Information Technology Monash University ClaytonVIC Australia University of Toronto TorontoON Canada The Li Ka Shing Knowledge Institute of St. Michael's Hospital TorontoON Canada Kyushu University Fukuoka Japan Kitakyushu Koga Hospital Fukuoka Japan Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Computer Aided Medical Procedures Technical University of Munich Munich Germany Department of Computer Science The Johns Hopkins University Baltim

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process. © 2020, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Action recognition based on conceptors of skeleton joint trajectories

引用

Revista de la Facultad de Ingenieria 2016年第4期31卷 11-22页

作者： Bao, Jiao Pei, Lishen Ye, Mao Zhao, Xuezhuan School of Computer Science and Engineering Center for Robotics Key Laboratory for NeuroInformation of Ministry of Education University of Electronic Science and Technology of China Western High-Tech Industrial Zone Chengdu611731 China School of Computer Science Zhengzhou University of Aeronautics Zheng-Dong New Zone Zhengzhou450046 China

With the tremendous popularity of the Kinect, recognizing human actions or gestures from skeletal data becomes more feasible. Skeletal data is a more exact data than RGB video while it eliminates the occlusions that caused by the limbs of the actor. Previous neural network based approaches recognize actions by learning spatial-temporal features. However, nobody can explain what are those features represent. Different from them, we propose a novel action recognition framework based on conceptors of skeleton joint trajectories. Conceptor is a mechanism of neurodynamical organization. We compute conceptor for the trajectory of each dimension of the skeleton joint, and use the singular value vector to represent the trajectory. Then, we encode singular value vectors as binary vectors by using a clustering method. At last, we use softmax regression to recognize the trajectory codes. This is a novel framework which recognizes actions using the conceptual level information. Extensive experiments on benchmark datasets confirm the efficiency of this framework.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Memory-based Gait Recognition 27

Memory-based Gait Recognition

引用

27th British Machine Vision Conference, BMVC 2016

作者： Liu, Dan Ye, Mao Li, Xudong Zhang, Feng Lin, Lan School of Computer Science and Engineering Center for Robotics Key Laboratory for NeuroInformation of Ministry of Education University of Electronic Science and Technology of China Chengdu611731 China

Gait recognition is an interesting and challenging task aiming to classify the subjects based on the way they walk, which is subject to various covariates including carrying, clothing, surface and view angle. In this paper, we propose to utilize the memory mechanism to effectively alleviate the aforementioned problems. Specifically, we extract the 2D location information of human joints as the gait features via the migratory articulated human detection. Inspired by the mechanism of brain sequence processing, we input the gait feature sequence into the memory-based gait recognition (MGR) network, which achieves the process of memory and identification of the gait sequence. Our proposed MGR is robust to the noise that maybe exist in the gait sequence features. Besides, MGR is able to learn on the data with long range temporal dependencies. The experimental results on the CASIA A and CASIA B gait datasets verify the feasibility and effectiveness of the proposed method. © 2016. The copyright of this document resides with its authors.

关键词： Gait analysis

来源：评论

学校读者我要写书评

暂无评论

Child-centred motion-based age and gender estimation with neural network learning 30

Child-centred motion-based age and gender estimation with ne...

引用

30th AAAI Conference on Artificial Intelligence, AAAI 2016

作者： Sandygulova, Anara Absattar, Yerdaulet Doszhan, Damir Parisi, German I. Department of Robotics and Mechatronics School of Science and Technology Nazarbayev University Astana Kazakhstan Department of Computer Science School of Science and Technology Nazarbayev University Astana Kazakhstan Knowledge Technology Group Department of Informatics University of Hamburg Hamburg Germany

ISBN: (纸本)9781577357599

The focus of this work is to investigate how children's perception of the robot changes with age and gender, and to enable the robot to adapt to these differences for improving humanrobot interaction (HRI). We propose a neural network-based learning architecture to estimate children's age and gender based on the body motion performing a set of actions. To evaluate our system, we collected a fully annotated depth dataset of 28 children (aged between 7 and 16 years old) and applied it to a learning-based method for age and gender estimation by modeling children's 3D skeleton motion data. We discuss our results that show an average accuracy of 95.2% and 90.3% for age and gender respectively in the context of a real-world scenario. Copyright © 2016, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Human robot interaction

来源：评论

学校读者我要写书评

暂无评论

Adaptive control for an uncertain robotic manipulator with input saturations

引用

Control Theory and technology 2016年第2期14卷 113-121页

作者： Trong-Toan TRAN Shuzhi Sam GE Wei HE Center for Robotics and School of Automation Engineering University of Electronic Science and Technology of China (UESTC) Chengdu Sichuan 611731 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 Faculty of Electronic Technology Industrial University of rio Chi Minh City Ho Chi Minh City Vietnam School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 China

In this paper, we address the control problem of an uncertain robotic manipulator with input saturations, unknown input scalings and disturbances. For this purpose, a model reference adaptive control like （MRAC-like） is used to handle the input saturations. The model reference is input to state stable （ISS） and driven by the errors between the required control signals and input saturations. The uncertain parameters are dealt with by using linear-in-the-parameters property of robotic dynamics, while unknown input scalings and disturbances are handled by non-regressor based approach. Our design ensures that all the signals in the closed-loop system are bounded, and the tracking error converges to the compact set which depends on the predetermined bounds of the control inputs. Simulation on a planar elbow manipulator with two joints is provided to illustrate the effectiveness of the proposed controller.

关键词： Robotic manipulator adaptive control uncertain parameter input saturation trajectory tracking

来源：评论

学校读者我要写书评

暂无评论

Convex parameterizations and fidelity bounds for nonlinear identification and reduced-order modelling

arXiv

引用

arXiv 2017年

作者： Tobenkin, Mark M. Manchester, Ian R. Megretski, Alexandre Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Australian Centre for Field Robotics and School of Aerospace Mechanical and Mechatronic Engineering University of Sydney NSW2006 Australia

Model instability and poor prediction of long-term behavior are common problems when modeling dynamical systems using nonlinear "black-box" techniques. Direct optimization of the long-term predictions, often called simulation error minimization, leads to optimization problems that are generally nonconvex in the model parameters and suffer from multiple local minima. In this work we present methods which address these problems through convex optimization, based on Lagrangian relaxation, dissipation inequalities, contraction theory, and semidefinite programming. We demonstrate the proposed methods with a model order reduction task for electronic circuit design and the identification of a pneumatic actuator from experiment. Copyright © 2017, The Authors. All rights reserved.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

EyeQual: Accurate, Explainable, Retinal Image Quality Assessment

EyeQual: Accurate, Explainable, Retinal Image Quality Assess...

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Pedro Costa Aurelio Campilho Bryan Hooi Asim Smailagic Kris Kitani Shenghua Liu Christos Faloutsos Adrian Galdran Department of Electrical and Computer Engineering Carnegie Mellon University INESC TEC Porto Faculty of Engineering University of Porto Department of Statistics and Data Science Carnegie Mellon University Robotics Institute Carnegie Mellon University Chinese Academy of Sciences Institute of Computing Technology China School of Computer Science Carnegie Mellon University

Given a retinal image, can we automatically determine whether it is of high quality (suitable for medical diagnosis)? Can we also explain our decision, pinpointing the region or regions that led to our decision? Images from human retinas are vital for the diagnosis of multiple health issues, like hypertension, diabetes, and Alzheimer's; low quality images may force the patient to come back again for a second scanning, wasting time and possibly delaying treatment. However, existing retinal image quality assessment methods are either black boxes without explanations of the results or depend heavily on feature engineering or on complex and error-prone anatomical structures' segmentation. Therefore, we propose EyeQual, that solves exactly this problem. EyeQual is novel, fast for inference, accurate and explainable, pinpointing low-quality regions on the image. We evaluated EyeQual on two real datasets where it achieved 100% accuracy taking just 36 milliseconds for each image.

关键词： Retina Image quality Diabetes Standards Retinopathy Machine learning Graphical models

来源：评论

学校读者我要写书评

暂无评论

Interactive robot learning of gestures, language and affordances

arXiv

引用

arXiv 2017年

作者： Saponaro, Giovanni Jamone, Lorenzo Bernardino, Alexandre Salvi, Giampiero Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom KTH Royal Institute of Technology Stockholm Sweden

A growing field in robotics and Artificial Intelligence (AI) research is human-robot collaboration, whose target is to enable effective teamwork between humans and robots. However, in many situations human teams are still superior to human-robot teams, primarily because human teams can easily agree on a common goal with language, and the individual members observe each other effectively, leveraging their shared motor repertoire and sensorimotor resources. This paper shows that for cognitive robots it is possible, and indeed fruitful, to combine knowledge acquired from interacting with elements of the environment (affordance exploration) with the probabilistic observation of another agent's actions. We propose a model that unites (i) learning robot affordances and word descriptions with (ii) statistical recognition of human gestures with vision sensors. We discuss theoretical motivations, possible implementations, and we show initial results which highlight that, after having acquired knowledge of its surrounding environment, a humanoid robot can generalize this knowledge to the case when it observes another agent (human partner) performing the same motor actions previously executed during training. Copyright © 2017, The Authors. All rights reserved.

关键词： Gesture recognition

来源：评论

学校读者我要写书评

暂无评论

A report on the first international workshop on research methods in animal-computer interaction

A report on the first international workshop on research met...

引用

2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI EA 2017

作者： Zamansky, Anna Roshier, Amanda Mancini, Clara Collins, Emily C. Hall, Carol Grillaert, Katie Morrison, Ann North, Steve Wirman, Hanna Information Systems Department University of Haifa Israel School of Veterinary Medicine and Science University of Nottingham United Kingdom Animal-Computer Interaction Lab Open University Milton Keynes United Kingdom Sheffield Robotics University of Sheffield United Kingdom School of Animal Rural and Environmental Sciences Nottingham Trent University United Kingdom Messerli Research Institute University of Veterinary Medicine Medical University of Vienna University of Vienna Austria Department of Architecture Design and Media Technology Aalborg University Denmark Mixed Reality Laboratory University of Nottingham United Kingdom School of Design Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781450346566

Animal-computer Interaction (ACI) is a new and quickly developing discipline, which is closely related to HCI and is making reference to some of its theoretical frameworks and research methodologies. The first edition of the Workshop on Research Methods in ACI (RM4ACI) was co-located with the Third International Conference on Animal-computer Interaction, which took place in Milton-Keynes, UK in November 2016. This paper presents an overview of the workshop, including insights from discussions on some of the challenges faced by the ACI community as it works to develop ACI as a discipline, and on important opportunities for cross-fertilization between HCI and ACI that the HCI community could consider. Copyright © 2017 by the Association for Computing Machinery, Inc. (ACM).

关键词： Animals

来源：评论

学校读者我要写书评

暂无评论

Effective footstep planning using homotopy-class guidance

arXiv

引用

arXiv 2017年

作者： Ranganeni, Vinitha Chintalapudi, Sahit Salzman, Oren Likhachev, Maxim Paul G. Allen School of Computer Science & Engineering University of Washington SeattleWA United States College of Computing Georgia Institute of Technology AtlantaGA United States Robotics Institute Carnegie Mellon University PittsburghPA United States

Planning the motion for humanoid robots is a computationally-complex task due to the high dimensionality of the system. Thus, a common approach is to first plan in the low-dimensional space induced by the robot's feet-a task referred to as footstep planning. This low-dimensional plan is then used to guide the full motion of the robot. One approach that has proven successful in footstep planning is using search-based planners such as A* and its many variants. To do so, these search-based planners have to be endowed with effective heuristics to efficiently guide them through the search space. However, designing effective heuristics is a time-consuming task that requires the user to have good domain knowledge. Thus, our goal is to be able to effectively plan the footstep motions taken by a humanoid robot while obviating the burden on the user to carefully design local-minima free heuristics. To this end, we propose to use user-defined homotopy classes in the workspace that are intuitive to define. These homotopy classes are used to automatically generate heuristic functions that efficiently guide the footstep planner. Additionally, we present an extension to homotopy classes such that they are applicable to complex multi-level environments. We compare our approach for footstep planning with a standard approach that uses a heuristic common to footstep planning. In simple scenarios, the performance of both algorithms is comparable. However, in more complex scenarios our approach allows for a speedup in planning of several orders of magnitude when compared to the standard approach. Copyright © 2017, The Authors. All rights reserved.

关键词： Anthropomorphic robots

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：