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32nd Annual ACM Symposium on Applied Computing, SAC 2017

作者： Weis, Átila Astor Werhli, Adriano Velasque Filho, Nelson L. Duarte Federal University of Rio Grande-FURG Computer Science Center-C3 Group Automation and Intelligent Robotics-NAUTEC Brazil

ISBN: (纸本)9781450344869

Welding is a very important process in the industrial scenario, especially in the shipbuilding industry. Many researches employ robots to automate different welding process. This paper proposes a system for automated seam tracking, based on passive vision, to act as a data feedback to the automated robotic welding system, with quality and productivity gains in the process. Different cameras arrangements are presented and tested as the vision acquisition system in a commercial linear welding robot. A physical method and an algorithm to remove visual noises from images, produced by the GMAW welding process, are presented and the results discussed. © 2017 ACM.

关键词： Computer vision

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GuidedNet: Single Image Dehazing Using an End-to-End Convolutional Neural Network

GuidedNet: Single Image Dehazing Using an End-to-End Convolu...

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Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI)

作者： Lucas Teixeira Gonçalves Joel Felipe de Oliveira Gaya Paulo Jorge Lilles Drews Junior Silvia Silva da Costa Botelho Intelligent Robotics and Automation Group - NAUTEC Universidade Federal do Rio Grande - FURG Rio Grande Brazil Universidade Federal do Rio Grande Rio Grande RS BR

Poor visibility is a common problem when capturing images in participating mediums such as mist or water. The problem of generating a haze-free image based on a hazy one can be described as image dehazing. Previous approaches dealt with this problem using physical models based on priors and simplifications. In this paper, we demonstrate that an end-to-end convolutional neural network is able to learn the dehazing process with no parameters or priors required, resulting in a more generic method. Even though our model is trained entirely with hazy indoor images, we are able to fully restore outdoor images with real haze. Also, we propose an architecture containing the novel Guided Layers, introduced in order to reduce the loss of spatial information while restoring the images. Our method outperforms other machine learning based models, yielding superior results both qualitatively and quantitatively.

关键词： Atmospheric modeling Neural networks Image restoration Training Scattering Degradation Network architecture

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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Towards comparison of Kalman filter methods for localisation in underwater environments 14

Towards comparison of Kalman filter methods for localisation...

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14th Latin American robotics Symposium and 5th Brazilian Symposium on robotics, LARS-SBR 2017

作者： Thiago Silva Da Rosa, Romulo Zaffari, Guilherme Brunei Dias De Oliveira Evald, Paulo Jefferson Jorge Drews, Paulo Lilles Silva Da Costa Botelho, Silvia Intelligent Robotics and Automation Group - NAUTEC Center of Computational Sciences - C3 Federal University of Rio Grande - FURG Rio Grande RS Brazil

ISBN: (纸本)9781538609569

Kalman Filters are utilised for filtering and estimation in a large set of application. Here, this methodology is utilised for trajectory estimation of an underwater robot. In this work, three Kalman Filter methods are proposed for trajectory estimation. There are: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Central Difference Kalman Filter (CDKF). Simulation results are presented and discussed, where UKF and CDKF presented better performance than EKF with data that were collected from our dataset. However, UKF had a slightly smaller execution time than CDKF with almost the same error. © 2017 IEEE.

关键词： Extended Kalman filters

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Author Correction: Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control

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Scientific reports 2020年第1期10卷 6694页

作者： Elmar Rueckert Jernej Čamernik Jan Peters Jan Babič Intelligent Autonomous Systems Lab Technische Universität Darmstadt Darmstadt 64289 Germany. rueckert@ias.tu-darmstadt.de. Department for Automation Biocybernetics and Robotics Jožef Stefan Institute Ljubljana SI-1000 Slovenia. Jožef Stefan International Postgraduate School Jamova cesta 39 1000 Ljubljana Slovenia. Intelligent Autonomous Systems Lab Technische Universität Darmstadt Darmstadt 64289 Germany. Robot Learning Group Max-Planck Institute for Intelligent Systems Tuebingen 72076 Germany.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Velocity regulation of a linear welding robot by unscented and cubature Kalman filter output estimation-based sliding mode control

Velocity regulation of a linear welding robot by unscented a...

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Annual Conference of Industrial Electronics Society

作者： Paulo Jefferson Dias de Oliveira Evald Rômulo Thiago Silva da Rosa Jusoan Lang Mór Rodrigo Zelir Azzolin Vincius Menezes de Oliveira Silvia Silva da Costa Botelho Intelligent Robotics and Automation Group Federal University of Rio Grande Brazil

Welding is an important operation in manufacturing processes and it can represent a highly relevant amount of production costs, depending on material workpieces and required weld quantity. In mechanised and robotised welding processes, robot's travel velocity is an important parameter, which requires a proper regulation to obtain a good quality for weld beam. Then, in this work, a nonlinear model of a robot motor with their identified parameters is presented and a sliding mode control is proposed to regulate robot velocity. Furthermore, an Unscented Kalman Filter and a Cubature Kalman Filter are implemented, separately, for output system estimation. The control system with both estimators presented satisfactory tracking performances, in simulations, converging fast and with very small chattering.

关键词： Kalman filters Robots DC motors Welding Control systems Friction Torque

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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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

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An extended Kalman filter state estimation-based robust MRAC for welding robot motor control

An extended Kalman filter state estimation-based robust MRAC...

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Annual Conference of Industrial Electronics Society

作者： Paulo Jefferson Dias de Oliveira Evald Jusoan Lang Mór Rômulo Thiago Silva da Rosa Rodrigo Zelir Azzolin Vincius Menezes de Oliveira Silvia Silva da Costa Botelho Intelligent Robotics and Automation Group Federal University of Rio Grande Rio Grande Brazil

The robotic welding processes have been highly widespread in manufacturing industries due to large-scale production. An area where these processes are widely applied are shipyards, where there are necessary hundreds to thousands of kilograms of weld by hour. However, systems that operate in open-air environments are vulnerable to sundry disturbances, noises in measures, as well as possible unavailability of measurement of some system states, required for the controller. Taking it into account, in this work, a Robust Model Reference Adaptive Control is proposed to regulate the velocity of a nonlinear motor of a linear welding robot. Furthermore, an Extended Kalman Filter is implemented to estimate the system states and attenuate measurement noises. The proposed control system demonstrated a very good performance with fast convergence and small error.

关键词： Kalman filters Welding DC motors Robots Robustness Friction Adaptation models

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Understanding metric-related pitfalls in image analysis validation

arXiv

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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

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Semantic Mapping on Underwater Environment Using Sonar Data 13

Semantic Mapping on Underwater Environment Using Sonar Data

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13th Latin American robotics Symposium and 4th Brazilian Symposium on robotics, LARS/SBR 2016

作者： Machado, M. Drews, P. Núñez, P. Botelho, S. NAUTEC Intelligent Robotics and Automation Group Univ. Federal do Rio Grande - FURG Rio Grande Brazil Department of Computer and Communication Technology Universidad de Extremadura Cáceres Extremadura Spain

ISBN: (纸本)9781509036561

The use of robots in underwater exploration is increasing in the last years. The automation of the monitoring, inspection and underwater maintenance tasks requires the understanding of the environment. One of the key issues of these systems is to recognize in the objects in the scene. This paper proposes a method to provide a semantic mapping using acoustic images acquired by forward looking sonar (FLS). The method represents the environment using Gaussian probability density functions. Furthermore, we efficiently segment and classify the structures in the scene. Finally, we create a semantic map of the scene. We evaluate the method in a real dataset acquired by an underwater vehicle performing autonomous navigation and mapping tasks in a harbor area. © 2016 IEEE.

关键词： Mapping

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