检索结果-内蒙古大学图书馆

IEEE International Conference on robotics and Automation (ICRA)

作者： Seong-Heum Kim Gyeongmin Choe Byungtae Ahn In So Kweon Robotics and Computer Vision Laboratory School of Electrical Engineering KAIST Daejeon Republic of Korea

In this paper, we present a visual learning framework to retrieve a 3D model and estimate its pose from a single image. To increase the quantity and quality of training data, we define our simulation space in the near infrared (NIR) band, and utilize the quasi-Monte Carlo (MC) method for scalable photorealistic rendering of manufactured components. Two types of convolutional neural network (CNN) architectures are trained over these synthetic data and a relatively small amount of real data. The first CNN model seeks the most discriminative information and uses it to classify industrial components with fine-grained shape attributes. Once a 3D model is identified, one of the category-specific CNNs is tested for pose regression in the second phase. The mixed data for learning object categories is useful in domain adaptation and attention mechanism in our system. We validate our data-driven method with 88 component models, and the experimental results are qualitatively demonstrated. Also, the CNNs trained with various conditions of mixed data are quantitatively analyzed.

关键词： Three-dimensional displays Rendering (computer graphics) Computational modeling Solid modeling Shape Cameras Adaptation models

来源：评论

学校读者我要写书评

暂无评论

Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

引用

IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

VPGNet: Vanishing point guided network for lane and road marking detection and recognition

arXiv

引用

arXiv 2017年

作者： Lee, Seokju Kim, Junsik Yoon, Jae Shin Shin, Seunghak Bailo, Oleksandr Kim, Namil Lee, Tae-Hee Hong, Hyun Seok Han, Seung-Hoon Kweon, In So Robotics and Computer Vision Lab KAIST Samsung Electronics DMC R&D Center

In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions. We tackle rainy and low illumination conditions, which have not been extensively studied until now due to clear challenges. For example, images taken under rainy days are subject to low illumination, while wet roads cause light reflection and distort the appearance of lane and road markings. At night, color distortion occurs under limited illumination. As a result, no benchmark dataset exists and only a few developed algorithms work under poor weather conditions. To address this shortcoming, we build up a lane and road marking benchmark which consists of about 20,000 images with 17 lane and road marking classes under four different scenarios: no rain, rain, heavy rain, and night. We train and evaluate several versions of the proposed multi-task network and validate the importance of each task. The resulting approach, VPGNet, can detect and classify lanes and road markings, and predict a vanishing point with a single forward pass. Experimental results show that our approach achieves high accuracy and robustness under various conditions in real-time (20 fps). The benchmark and the VPGNet model will be publicly available 1. Copyright © 2017, The Authors. All rights reserved.

关键词： Road and street markings

来源：评论

学校读者我要写书评

暂无评论

One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks

arXiv

引用

arXiv 2018年

作者： Valverde, Sergi Salem, Mostafa Cabezas, Mariano Pareto, Deborah Vilanova, Joan C. Ramió-Torrentà, Lluís Rovira, Àlex Salvi, Joaquim Oliver, Arnau Lladó, Xavier Research institute of Computer Vision and Robotics University of Girona Spain Computer Science Department Faculty of Computers and Information Assiut University Egypt Magnetic Resonance Unit Dept of Radiology Vall d’Hebron University Hospital Spain Girona Magnetic Resonance Center Spain Multiple Sclerosis and Neuroimmunology Unit Dr. Josep Trueta University Hospital Spain

In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN methods tend to decrease significantly when evaluated on different image domains compared with those used for training, which demonstrates the lack of adaptability of CNNs to unseen imaging data. In this study, we analyzed the effect of intensity domain adaptation on our recently proposed CNN-based MS lesion segmentation method. Given a source model trained on two public MS datasets, we investigated the transferability of the CNN model when applied to other MRI scanners and protocols, evaluating the minimum number of annotated images needed from the new domain and the minimum number of layers needed to re-train to obtain comparable accuracy. Our analysis comprised MS patient data from both a clinical center and the public ISBI2015 challenge database, which permitted us to compare the domain adaptation capability of our model to that of other state-of-the-art methods. In both datasets, our results showed the effectiveness of the proposed model in adapting previously acquired knowledge to new image domains, even when a reduced number of training samples was available in the target dataset. For the ISBI2015 challenge, our one-shot domain adaptation model trained using only a single image showed a performance similar to that of other CNN methods that were fully trained using the entire available training set, yielding a comparable human expert rater performance. We believe that our experiments will encourage the MS community to incorporate its use in different clinical settings with reduced amounts of annotated data. This approach could be meaningful not only in terms of the accuracy in delineating MS lesions but also in the related reductions in time and economic costs deri

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Application the Spiking Neuron Model with Structural Adaptation to Describe Neuromorphic Systems

引用

Procedia computer Science 2017年 103卷 190-197页

作者： A.V. Bakhshiev F.V. Gundelakh Computer Vision Laboratory Russian State Scientific Center for Robotics and Technical Cybernetics (RTC) 21 Tikhoretskiy pr. Saint-Petersburg 194064 Russia

The paper discusses the architectural elements of neuromorphic systems on the example of behaviour control of robots. The basis of the work is new spiking neuron model, which allows describing the known biological network relying on macroscopic parameters of neurons - the size, the relative length of dendrites, etc. The model also allows no parametric setting but change synaptic and dendritic structures. This implies the possibility of a structural adjustment neuromorphic system. The paper gives examples of simulation results of the proposed elements neuromorphic systems.

关键词： spiking neuron neuromorphic system structural adaptation dendritic tree motion control behavior control.

来源：评论

学校读者我要写书评

暂无评论

Effect of Field of View in Stereovision-Based Visual Homing

Effect of Field of View in Stereovision-Based Visual Homing

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Damian Lyons Luca DelSignore Benjamin Barriage Robotics and Computer Vision Lab Fordham University Bronx NY Fordham University New York NY US

Navigation is challenging for an autonomous robot operating in an unstructured environment. Visual homing is an AI local navigation technique used to direct a robot to a previously seen location, and is inspired by biological models. Most visual homing uses a panoramic camera. Prior work has shown that exploiting depth cues in homing from, e.g., a stereo-camera, leads to improved performance. However, many stereo-cameras have a limited field of view (FOV). We present a stereovision database methodology for visual homing. We use two databases we have collected, one indoor and one outdoor, to evaluate the effect of FOV on the performance of our homing with stereovision algorithm. Based on over 120,000 homing trials, we show that contrary to intuition, a panoramic field of view does not necessarily lead to the best performance, and we discuss the implications of this.

关键词： Visualization Cameras Robot kinematics Visual databases Navigation

来源：评论

学校读者我要写书评

暂无评论

Coordinated Efficient Buoys Data Collection in Large Complex Coastal Environments using UAVs

Coordinated Efficient Buoys Data Collection in Large Complex...

引用

Oceans (Conference)

作者： J. Braga F. Balampanis A. Pedro Aguiar J. Sousa I. Maza A. Ollero Department of Electrical and Computer Engineering University of Porto Portugal Robotics Vision and Control Group (GRVC) University of Seville Spain

ISBN: (纸本)9781509064298;9780692946909

Deploying a large number of sensing buoys is a powerful tool for oceanographic, marine biology and climate change research. In this work we address the problem of efficient data collection by a team of Unmanned Aerial Vehicles (UAV) from low-power, small buoys with previously unknown locations in complex coastal or remote oceanic regions with aerial restrictions. To tackle this problem we propose and develop a 3-layers architecture that search for the buoys, enables communication with them and creates a UAV communication network to recover the data. Extensive simulations show that the proposed work both optimizes the searching area coverage path planning for complex regions and the quality-of-service for the data transmission from the buoys to the final destination using UAVs as relay nodes.

关键词： buoys final destination

来源：评论

学校读者我要写书评

暂无评论

Real-time Fish Detection in Trawl Nets

Real-time Fish Detection in Trawl Nets

引用

Oceans (Conference)

作者： R. Prados R. Garcia N. Gracias L. Neumann Havard Vagstol Computer Vision and Robotics Institute University of Girona Girona Spain ICREA Pg. Lluís Companys 23 Barcelona Spain Scantrol Deep Vision AS Bergen Norway

This work presents advances towards the development of the Deep vision system, consisting of a stereo camera setup attached to a trawl, which will make it possible to identify and measure the fish passing through without bringing them aboard the boat. This identification and measuring task requires a preprocessing in which the specimens should be segmented from the image background. Several lighting artifacts such as non-uniform illumination, shadows and reflections are some of the challenges to be addressed during the segmentation procedure. This paper describes the segmentation pipeline used to segment the fish from the background in an efficient and robust way.

关键词： Fish segmentation Trawl camera system HSV

来源：评论

学校读者我要写书评

暂无评论

Integrating mission, logistics, and task planning for skills-based robot control in industrial kitting applications 34

Integrating mission, logistics, and task planning for skills...

引用

34th Workshop of the UK Planning and Scheduling Special Interest Group, PlanSIG 2016

作者： Crosby, Matthew Petrick, Ronald P. A. Toscano, César Dias, Rui Correia Rovida, Francesco Krüger, Volker Department of Computer Science Heriot-Watt University EdinburghEH14 4AS United Kingdom INESC TEC Technology and Science Porto4200 - 465 Portugal Robotics Vision and Machine Intelligence Lab. Aalborg University Copenhagen2450 Denmark

This paper presents an integrated cognitive robotics system for industrial kitting operations in a modern factory setting. The robot system combines low-level robot control and execution monitoring with automated mission and task planning, and a logistics planner which communicates with the factory's manufacturing execution system. The system has been implemented and tested on a series of automotive kitting problems, where collections of parts are picked from a warehouse and delivered to the production line. The system has been empirically evaluated and the complete framework shown to be successful at assembling kits in a small factory environment. © 2017, CEUR-WS. All rights reserved.

关键词： Robot programming

来源：评论

学校读者我要写书评

暂无评论

A Feedback Estimation Approach for Therapeutic Facial Training

A Feedback Estimation Approach for Therapeutic Facial Traini...

引用

International Conference on Automatic Face and Gesture Recognition

作者： Cornelia Dittmar Joachim Denzler Horst-Michael Gross Neuroinformatics and Cognitive Robotics Lab Ilmenau University of Technology Germany Computer Vision Group Friedrich Schiller University Jena Germany

Neuromuscular retraining is an important part of facial paralysis rehabilitation. To date, few publications have addressed the development of automated systems that support facial training. Current approaches require external devices attached to the patient's face, lack quantitative feedback, and are constrained to one or two facial training exercises. We propose an automated camera-based training system that provides global and local feedback for 12 different facial training exercises. Based on extracted 3D facial features, the patient's performance is evaluated and quantitative feedback is derived. The description of the feedback estimation is supplemented by a detailed experimental evaluation of the 3D feature extraction.

关键词： Feature extraction Training Three-dimensional displays Estimation Face Facial features Medical treatment

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：