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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Moonyoung Lee Yujin Heo Jinyong Park Hyun-Dae Yang Ho-Deok Jang Philipp Benz Hyunsub Park In So Kweon Jun-Ho Oh Research Center Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon Korea Robotics and Computer Vision Laboratory that is in charge of object perception part Korea Advanced Institute of Science and Technology 291 Daehakro Yuseong-gu Daejeon Korea

As the aging population grows at a rapid rate, there is an ever growing need for service robot platforms that can provide daily assistance at practical speed with reliable performance. In order to assist with daily tasks such as fetching a beverage, a service robot must be able to perceive its environment and generate corresponding motion trajectories. This becomes a challenging and computationally complex problem when the environment is unknown and thus the path planner must sample numerous trajectories that often are sub-optimal, extending the execution time. To address this issue, we propose a unique strategy of integrating a 3D object detection pipeline with a kinematically optimal manipulation planner to significantly increase speed performance at run-time. In addition, we develop a new robotic butler system for a wheeled humanoid that is capable of fetching requested objects at 24% of the speed a human needs to fulfill the same task. The proposed system was evaluated and demonstrated in a real-world environment setup as well as in public exhibition.

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Understanding Human Motion and Gestures for Underwater Human-Robot Collaboration

arXiv

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arXiv 2018年

作者： Islam, Md Jahidul Interactive Robotics and Vision Laboratory Department of Computer Science and Engineering University of Minnesota- Twin Cities United States

In this paper, we present a number of robust methodologies for an underwater robot to visually detect, follow, and interact with a diver for collaborative task execution. We design and develop two autonomous diver-following algorithms, the first of which utilizes both spatial- and frequency-domain features pertaining to human swimming patterns in order to visually track a diver. The second algorithm uses a convolutional neural network-based model for robust tracking-by-detection. In addition, we propose a hand gesture-based human-robot communication framework that is syntactically simpler and computationally more efficient than the existing grammar-based frameworks. In the proposed interaction framework, deep visual detectors are used to provide accurate hand gesture recognition;subsequently, a finite-state machine performs robust and efficient gesture-to-instruction mapping. The distinguishing feature of this framework is that it can be easily adopted by divers for communicating with underwater robots without using artificial markers or requiring memorization of complex language rules. Furthermore, we validate the performance and effectiveness of the proposed methodologies through extensive field experiments in closed- and open-water environments. Finally, we perform a user interaction study to demonstrate the usability benefits of our proposed interaction framework compared to existing methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural networks

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Corrigendum to "PSFHS challenge report: pubic symphysis and fetal head segmentation from intrapartum ultrasound images" [Medical Image Analysis 99 (2025),103353]

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Medical image analysis 2025年 104卷 103651页

作者： Jieyun Bai Zihao Zhou Zhanhong Ou Gregor Koehler Raphael Stock Klaus Maier-Hein Marawan Elbatel Robert Martí Xiaomeng Li Yaoyang Qiu Panjie Gou Gongping Chen Lei Zhao Jianxun Zhang Yu Dai Fangyijie Wang Guénolé Silvestre Kathleen Curran Hongkun Sun Jing Xu Pengzhou Cai Lu Jiang Libin Lan Dong Ni Mei Zhong Gaowen Chen Víctor M Campello Yaosheng Lu Karim Lekadir Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China Auckland Bioengineering Institute The University of Auckland Auckland New Zealand. Electronic address: jbai996@aucklanduni.ac.nz. Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Computer Science and Engineering The Chinese University of Hong Kong China. Computer Vision and Robotics Group University of Girona Girona Spain. Canon Medical Systems (China) Co. LTD Beijing China. College of Artificial Intelligence Nankai University Tianjin China. College of Computer Science and Electronic Engineering Hunan University Changsha China. School of Medicine University College Dublin Dublin Ireland. School of Computer Science University College Dublin Dublin Ireland. School of Statistics & Mathematics Zhejiang Gongshang University Hangzhou China. School of Computer Science & Engineering Chongqing University of Technology Chongqing China. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound & Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging & School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China. NanFang Hospital of Southern Medical University Guangzhou China. Zhujiang Hospital of Southern Medical University Guangzhou China. Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain. Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China. Electronic address: tluys@***. Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain.

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Deep representation of industrial components using simulated images

Deep representation of industrial components using simulated...

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2017 IEEE International Conference on robotics and Automation, ICRA 2017

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

ISBN: (纸本)9781509046331

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. © 2017 IEEE.

关键词： Monte Carlo methods

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Generative 3D hand tracking with spatially constrained pose sampling 28

Generative 3D hand tracking with spatially constrained pose ...

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28th British Machine vision Conference, BMVC 2017

作者： Roditakis, Konstantinos Makris, Alexandros Argyros, Antonis A. Computational Vision and Robotics Laboratory Institute of Computer Science FORTH Greece Computer Science Department University of Crete Greece

ISBN: (纸本)190172560X

We present a method for 3D hand tracking that exploits spatial constraints in the form of end effector (fingertip) locations. The method follows a generative, hypothesize-and-test approach and uses a hierarchical particle filter to track the hand. In contrast to state of the art methods that consider spatial constraints in a soft manner, the proposed approach enforces constraints during the hand pose hypothesis generation phase by sampling in the Reachable Distance Space (RDS). This sampling produces hypotheses that respect both the hands’ dynamics and the end effector locations. The data likelihood term is calculated by measuring the discrepancy between the rendered 3D model and the available observations. Experimental results on challenging, ground truth-annotated sequences containing severe hand occlusions demonstrate that the proposed approach outperforms the state of the art in hand tracking accuracy. © 2017. The copyright of this document resides with its authors.

关键词： End effectors

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Real-Time Automatic License Plate Recognition through Deep Multi-Task Networks

Real-Time Automatic License Plate Recognition through Deep M...

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Brazilian Symposium on computer Graphics and Image Processing (SIBGRAPI)

作者： Gabriel Resende Gonçalves Matheus Alves Diniz Rayson Laroca David Menotti William Robson Schwartz Department of Computer Science Universidade Federal de Minas Gerais Brazil Laboratory of Vision Robotics and Imaging Universidade Federal do Paraná Brazil

With the increasing number of cameras available in the cities, video traffic analysis can provide useful insights for the transportation segment. One of such analysis is the Automatic License Plate Recognition (ALPR). Previous approaches divided this task into several cascaded subtasks, i.e., vehicle location, license plate detection, character segmentation and optical character recognition. However, since each task has its own accuracy, the error propagation between each subtask is detrimental to the final accuracy. Therefore, focusing on the reduction of error propagation, we propose a technique that is able to perform ALPR using only two deep networks, the first performs license plate detection (LPD) and the second performs license plate recognition (LPR). The latter does not execute explicit character segmentation, which reduces significantly the error propagation. As these deep networks need a large number of samples to converge, we develop new data augmentation techniques that allow them to reach their full potential as well as a new dataset to train and evaluate ALPR approaches. According to experimental results, our approach is able to achieve state-of-the-art results in the SSIG-SegPlate dataset, reaching improvements up to 1.4 percentage point when compared to the best baseline. Furthermore, the approach is also able to perform in real time even in scenarios where many plates are present at the same frame, reaching significantly higher frame rates when compared with previously proposed approaches.

关键词： Licenses Detectors Task analysis Character recognition Proposals Image segmentation Real-time systems

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Dynamic Fusion of Electromyographic and Electroencephalographic Data towards Use in Robotic Prosthesis Control

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Michael Pritchard Abraham Itzhak Weinberg John A R Williams Felipe Campelo Harry Goldingay Diego R Faria Aston Robotics Vision and Intelligent Systems Laboratory (ARVIS Lab) College of Engineering and Physical Sciences Aston University Birmingham B4 7ET UK Department of Computer Science College of Engineering and Physical Sciences Aston University Birmingham B4 7ET UK Aston Institute of Photonic Technologies Aston University Birmingham B4 7ET UK

We demonstrate improved performance in the classification of bioelectric data for use in systems such as robotic prosthesis control, by data fusion using low-cost electromyography (EMG) and electroencephalography (EEG) devices. Prosthetic limbs are typically controlled through EMG, and whilst there is a wealth of research into the use of EEG as part of a brain-computer interface (BCI) the cost of EEG equipment commonly prevents this approach from being adopted outside the lab. This study demonstrates as a proof-of-concept that multimodal classification can be achieved by using low-cost EMG and EEG devices in tandem, with statistical decision-level fusion, to a high degree of accuracy. We present multiple fusion methods, including those based on Jensen-Shannon divergence which had not previously been applied to this problem. We report accuracies of up to 99% when merging both signal modalities, improving on the best-case single-mode classification. We hence demonstrate the strengths of combining EMG and EEG in a multimodal classification system that could in future be leveraged as an alternative control mechanism for robotic prostheses.

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ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition — RRC-MLT-2019

ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Te...

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International Conference on Document Analysis and Recognition

作者： Nibal Nayef Yash Patel Michal Busta Pinaki Nath Chowdhury Dimosthenis Karatzas Wafa Khlif Jiri Matas Umapada Pal Jean-Christophe Burie Cheng-lin Liu Jean-Marc Ogier no affiliation The Robotics Institute Carnegie Mellon Universiry Pittsburgh USA Department of Cybernetics Czech Technical University Prague Czech Republic CVPR unit Indian Statistical Institute India Computer Vision Center Universitat Autònoma de Barcelona Spain L3i Laboratory University of La Rochelle France National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences China

With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge.

关键词： Task analysis Text recognition Training Benchmark testing Protocols Rendering (computer graphics)

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Convolutional Neural Networks for Automatic Meter Reading

arXiv

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arXiv 2019年

作者： Laroca, Rayson Barroso, Victor Diniz, Matheus A. Gonçalves, Gabriel R. Schwartz, William Robson Menotti, David Federal University of Paraná Laboratory of Vision Robotics and Imaging Department of Informatics Av. Coronel Francisco Heráclito dos Santos 100 Curitiba81530-000 Brazil Federal University of Minas Gerais Smart Surveillance Interest Group Department of Computer Science Av. Antônio Carlos 6627 Belo Horizonte31270-010 Brazil

In this paper, we tackle Automatic Meter Reading (AMR) by leveraging the high capability of Convolutional Neural Networks (CNNs). We design a two-stage approach that employs the Fast-YOLO object detector for counter detection and evaluates three different CNN-based approaches for counter recognition. In the AMR literature, most datasets are not available to the research community since the images belong to a service company. In this sense, we introduce a new public dataset, called UFPR-AMR dataset, with 2,000 fully and manually annotated images. This dataset is, to the best of our knowledge, three times larger than the largest public dataset found in the literature and contains a well-defined evaluation protocol to assist the development and evaluation of AMR methods. Furthermore, we propose the use of a data augmentation technique to generate a balanced training set with many more examples to train the CNN models for counter recognition. In the proposed dataset, impressive results were obtained and a detailed speed/accuracy trade-off evaluation of each model was performed. In a public dataset, state-of-the-art results were achieved using less than 200 images for training. Copyright © 2019, The Authors. All rights reserved.

关键词： Public utilities

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Augmented Vector Field Navigation Cost Mapping using Inertial Sensors

Augmented Vector Field Navigation Cost Mapping using Inertia...

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International Conference on Advanced robotics (ICAR)

作者： Felipe G. Oliveira Armando Alves Neto Paulo Borges Mario F. M. Campos Douglas G. Macharet Institute of Exact Sciences and Technology Universidade Federal do Amazonas AM Brazil Universidade Federal de Minas Gerais Universidade Federal de Minas Gerais MG Brazil Data 61 Commonwealth Scientific and Industrial Research Organization Brisbane Queensland Australia Computer Vision and Robotics Laboratory (VeRLab) Universidade Federal de Minas Gerais MG Brazil

In outdoor field robotics, considering the environmental characteristics is key to improving the efficiency of autonomous navigation. In this context, identifying rough terrain can significantly increase the reliability of operations. This paper addresses the problem of mapping the navigation cost associated with uneven outdoor terrains. We propose an augmented vector field representation obtained only with the use of inertial sensors. The map is determined considering characteristics such as roughness and slope. Experiments were carried out with different robots in real-world environments presenting different terrain characteristics to analyze the quality and efficiency of the mapping process. Results show that the obtained navigation cost maps provide a reliable indication of the ground characteristics of outdoor environments and can be used in the path planning stage.

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