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arXiv 2020年

作者： Wang, Guangming Yang, Yehui Zhang, Huixin Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Computer Science and Technology University of Cambridge United Kingdom

The semantic segmentation of point clouds is an important part of the environment perception for robots. However, it is difficult to directly adopt the traditional 3D convolution kernel to extract features from raw 3D point clouds because of the unstructured property of point clouds. In this paper, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3D convolution operator. This newly proposed feature extraction operator improves the accuracy of the network and reduces the parameters of the network. In addition, this paper analyzes the defect of point cloud interpolation methods based on the distance as the interpolation weight and proposes the self-learned distance-feature density by combining the distance and the feature correlation. The proposed method makes the feature extraction of spherical interpolated convolution network more rational and effective. The effectiveness of the proposed network is demonstrated on the 3D semantic segmentation task of point clouds. Experiments show that the proposed method achieves good performance on the ScanNet dataset and Paris-Lille-3D dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Semi-supervised medical image classification with relation-driven self-ensembling model

arXiv

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arXiv 2020年

作者： Liu, Quande Yu, Lequan Luo, Luyang Dou, Qi Heng, Pheng Ann Department of Computer Science and Engineering Chinese University of Hong Kong Department of Radiation Oncology Stanford University StanfordCA94305 United States T Stone Robotics Institute Chinese University of Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Training deep neural networks usually requires a large amount of labeled data to obtain good performance. However, in medical image analysis, obtaining high-quality labels for the data is laborious and expensive, as accurately annotating medical images demands expertise knowledge of the clinicians. In this paper, we present a novel relation-driven semi-supervised framework for medical image classification. It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations, and leverages a self-ensembling model to produce high-quality consistency targets for the unlabeled data. Considering that human diagnosis often refers to previous analogous cases to make reliable decisions, we introduce a novel sample relation consistency (SRC) paradigm to effectively exploit unlabeled data by modeling the relationship information among different samples. Superior to existing consistency-based methods which simply enforce consistency of individual predictions, our framework explicitly enforces the consistency of semantic relation among different samples under perturbations, encouraging the model to explore extra semantic information from unlabeled data. We have conducted extensive experiments to evaluate our method on two public benchmark medical image classification datasets, i.e., skin lesion diagnosis with ISIC 2018 challenge and thorax disease classification with ChestX-ray14. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

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Guest Editorial Special Issue on Affordances

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IEEE Transactions on Cognitive and Developmental systems 2018年第1期10卷 1-3页

作者： Jamone, Lorenzo Ugur, Emre Santos-Victor, José ARQ-Advanced Robotics at Queen Mary School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Computer Engineering Department Boǧaziçi University Istanbul Turkey Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

The concept of affordances appeared in psychology during the late 60s, as an alternative perspective on the visual perception of the environment. More precisely, the term affordances was introduced by J. J. Gibson in 1966 [items 1)-3) of the Appendix]. Gibson defines the affordances of an object as “what it offers the animal, what it provides or furnishes, either for good or ill.” What the objects afford to the agent are action possibilities that are directly perceived through vision, in a precategorical and subconscious way, without the need to construct a fully detailed model of the world or to perform semantic reasoning or explicit object recognition. Central to Gibson’s theory is the notion that the sensorimotor capabilities of the agent dramatically influence perception: the concept of affordances is “something that refers to both the environment and the animal in a way that no existing term does. It implies the complementarity of the animal and the environment.” Notably, the human ability to perceive affordances emerges gradually during development, and it is the outcome of exploratory and observational learning; as this ability appears, predicting the effects of the actions becomes possible as well, eventually leading to problem solving skills.

关键词： Special issues and sections Psychology Robot sensing systems Cognition Biological system modeling

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Corrigendum to “Extraction of notable points from ECG data: A description of a dataset related to 30-s seated and 30-s stand up” [Data in Brief, volume 46 (2023) 108874]

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Data in Brief 2023年 47卷

作者： Rui Pedro Duarte Francisco Alexandre Marinho Eduarda Sofia Bastos Rui João Pinto Pedro Miguel Silva Alice Fermino Hanna Vitalyvna Denysyuk António Jorge Gouveia Norberto Jorge Gonçalves Paulo Jorge Coelho Eftim Zdravevski Petre Lameski Toni Tripunovski Nuno M. Garcia Ivan Miguel Pires Escola de Ciências e Tecnologia Universidade de Trás-Os-Montes e Alto Douro Quinta de Prados Vila Real 5001-801 Portugal Computer Science Department Universidade da Beira Interior Covilhã 6200-001 Portugal Instituto de Telecomunicações Universidade da Beira Interior Covilhã 6200-001 Portugal School of Technology and Management Polytechnic of Leiria Leiria 2411-901 Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) DEEC Pólo II Coimbra 3030-290 Portugal Faculty of Computer Science and Engineering University Ss Cyril and Methodius 1000 Skopje North Macedonia Institute of Pathophysiology and Nuclear Medicine University Ss Cyril and Methodius 1000 Skopje North Macedonia

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An Efficient Scheme for Path Planning in Internet of Drones

An Efficient Scheme for Path Planning in Internet of Drones

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IEEE Global Communications Conference

作者： Aditya Goyal Nikhil Kumar Amit Dua Neeraj Kumar Joel J. P. C. Rodrigues Dushantha Nalin K. Jayakody Computer Science and Information Systems Department Birla Institute of Technology and Science Pilani Computer science and Engineering Department Thapar Institute of Engineering and Technology Punjab National Institute of Telecommunications (INATEL) Brazil Instituto de Telecomunicações Portugal Federal University of Piauí (UFPI) Brazil Faculty of Engineering Sri Lanka Technological Campus Sri Lanka School of Computer Science & Robotics National Research Tomsk Polytechnic University Russia

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

The Internet of Drones (IoD) is a multi-layered, control architecture to regulate and coordinate the navigation of Unmanned Aerial Vehicles (UAV) in a shared public airspace. UAVs have the potential to be employed in public space for purposes like surveillance, monitoring, package delivery, emergency services, etc. For proper operation, an efficient path planning among IoD is required so that they can adaptively decide their path for data dissemination. Most of the existing solutions for this problem have made unreasonable assumptions and do not offer scalability. The scheme proposed in the paper provides a network architecture for the scalable solution of UAVs in an urban environment addressing issues of path planning, safety, privacy, and network connectivity. The scheme has been tested using exhaustive simulation and results prove that the proposed scheme is efficient in terms of reducing the overall cost and delivery time with the increasing weight of payload in the drones.

关键词： Drones Path planning Internet Navigation Atmospheric modeling Scalability Companies

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Front Cover: Micrometer-Sized Liposome-Based systems: A Hierarchical Breakdown (ChemsystemsChem 3/2025)

ChemSystemsChem

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ChemsystemsChem 2025年第3期7卷

作者： Prof. Dr. Shogo Hamada Dr. Hironori Sugiyama Prof. Dr. Yiting Zhang Prof. Dr. Shoji Iwabuchi Soichiro Hiroi Toshiki Maruyama Yuktesh Balaji Sota Kumagai Prof. Dr. Satoshi Murata Prof. Dr. Taro Toyota Department of Computer Science School of Computing & Department of Systems and Control Engineering School of Engineering Institute of Science Tokyo 4259 Nagatsuta-cho Midori-ku Yokohama-city Kanagawa 226-8501 Japan These authors contributed equally. Department of Applied Chemistry Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Department of Chemistry College of Science Rikkyo University 3-34-1 Nishi-Ikebukuro Toshima-ku Tokyo 171-8501 Japan Department of Biotechnology and Life Science Tokyo University of Agriculture and Technology 2-24-16 Naka-cho Koganei-shi Tokyo 185-8588 Japan Department of Basic Science Graduate School of Arts and Sciences The University of Tokyo 3-8-1 Komaba Meguro-ku Tokyo 153-8902 Japan Department of Robotics Graduate School of Engineering Tohoku University 6-6-01 Aramaki-Aza Aoba Aoba-ku Sendai Miyagi 980-8579 Japan Universal Biology Institute The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan

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Cooperative Intelligence for Autonomous Driving

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ZTE Communications 2019年第2期17卷 44-50页

作者： CHENG Xiang DUAN Dongliang YANG Liuqing ZHENG Nanning State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Department of Electrical and Computer Engineering University of WyomingLaramieWY 82071USA Department of Electrical and Computer Engineering Colorado State UniversityFort CollinsCO 80523USA Institute of Artificial Intelligence and Robotics Xi’an Jiaotong UniversityXi’anShaanxi 710049China

Autonomous driving is an emerging technology attracting interests from various sectors in recent *** of existing work treats autonomous vehicles as isolated individuals and has focused on developing separate intelligent *** this paper,we attempt to exploit the connectivity among vehicles and propose a systematic framework to develop autonomous driving *** first introduce a general hierarchical information fusion framework for cooperative sensing to obtain global situational awareness for *** this,a cooperative intelligence framework is proposed for autonomous driving *** general framework can guide the development of data collection,sharing and processing strategies to realize different intelligent functions in autonomous driving.

关键词： autonomous driving cooperative intelligence information fusion vehicular communications and networking

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Precision of sung notes in carnatic music 19

Precision of sung notes in carnatic music

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19th International Society for Music Information Retrieval Conference, ISMIR 2018

作者： Viraraghavan, Venkata Subramanian Murthy, Hema A. Aravind, R. TCS Research and Innovation Embedded Systems and Robotics Bangalore India Department of Electrical Engineering Indian Institute of Technology Madras India Department of Computer Science and Engineering Indian Institute of Technology Madras India

ISBN: (纸本)9782954035123

Carnatic music is replete with continuous pitch movement called gamakas and can be viewed as consisting of constant-pitch notes (CPNs) and transients. The stationary points (STAs) of transients – points where the pitch curve changes direction – also carry melody information. In this paper, the precision of sung notes in Carnatic music is studied in detail by treating CPNs and STAs separately. There is variation among the nineteen musicians considered, but on average, the precision of CPNs increases exponentially with duration and settles at about 10 cents for CPNs longer than 0.5 seconds. For analyzing STAs, in contrast to Western music, rāga (melody) information is found to be necessary, and errors in STAs show a significantly larger standard deviation of about 60 cents. To corroborate these observations, the music was automatically transcribed and re-synthesized using CPN and STA information using two interpolation techniques. The results of perceptual tests clearly indicate that the grammar is highly flexible. We also show that the precision errors are not due to poor pitch tracking, singer deficiencies or delay in auditory feedback. © V S Viraraghavan, H A Murthy, R Aravind.

关键词： Music

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Online multi-object tracking and segmentation with GMPHD filter and mask-based affinity fusion

arXiv

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arXiv 2020年

作者： Song, Young-Min Yoon, Young-Chul Yoon, Kwangjin Jeon, Moongu Lee, Seong-Whan Pedrycz, Witold The School of Electrical Engineering and Computer Science Gwangju Institute of Technology Gwangju61005 Korea Republic of The Robotics Laboratory Hyundai Motor Company Gyeonggi-do 16082 Korea Republic of SI Analytics Co. Ltd. Daejeon34051 Korea Republic of The Department of Artificial Intelligence Korea University Seoul02841 Korea Republic of The Department of Brain and Cognitive Engineering Korea University Seoul02841 Korea Republic of The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2H5 Canada The Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia The Systems Research Institute Polish Academy of Sciences Warsaw01-447 Poland

In this paper, we propose a highly practical fully online multi-object tracking and segmentation (MOTS) method that uses instance segmentation results as an input. The proposed method is based on the Gaussian mixture probability hypothesis density (GMPHD) filter, a hierarchical data association (HDA), and a mask-based affinity fusion (MAF) model to achieve high-performance online tracking. The HDA consists of two associations: segment-to-track and track-to-track associations. One affinity, for position and motion, is computed by using the GMPHD filter, and the other affinity, for appearance is computed by using the responses from a single object tracker such as a kernalized correlation filter. These two affinities are simply fused by using a score-level fusion method such as min-max normalization referred to as MAF. In addition, to reduce the number of false positive segments, we adopt mask IoU-based merging (mask merging). The proposed MOTS framework with the key modules: HDA, MAF, and mask merging, is easily extensible to simultaneously track multiple types of objects with CPU-only execution in parallel processing. In addition, the developed framework only requires simple parameter tuning unlike many existing MOTS methods that need intensive hyperparameter optimization. In the experiments on the two popular MOTS datasets, the key modules show some improvements. For instance, ID-switch decreases by more than half compared to a baseline method in the training sets. In conclusion, our tracker achieves state-of-the-art MOTS performance in the test sets. Copyright © 2020, The Authors. All rights reserved.

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Quantitative Evaluation of Endoscopic SLAM Methods: EndoSLAM Dataset

arXiv

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arXiv 2020年

作者： Ozyoruk, Kutsev Bengisu Incetan, Kagan Coskun, Gulfize Gokceler, Guliz Irem Almalioglu, Yasin Mahmood, Faisal Durr, Nicholas J. Curto, Eva Perdigoto, Luis Oliveira, Marina Araujo, Helder Alexandrino, Henrique Gilbert, Hunter B. Turan, Mehmet Institute of Biomedical Engineering Bogazici University Computer Science Department University of Oxford Oxford United Kingdom Department of Pathology Harvard Medical School BostonMA United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Institute for Systems and Robotics University of Coimbra Portugal Faculty of Medicine Clinical Academic Center of Coimbra University of Coimbra Coimbra Portugal Department of Mechanical and Industrial Engineering Louisiana State University Baton RougeLA United States

Deep learning techniques hold promise to improve dense topography reconstruction and pose estimation, as well as simultaneous localization and mapping (SLAM). However, currently available datasets do not support effective quantitative benchmarking. With this paper, we introduce a comprehensive endoscopic SLAM dataset containing both capsule and standard endoscopy recordings. A Panda robotic arm, two different commercially available high precision 3D scanners, two different commercially available capsule endoscopes with different camera properties and two different conventional endoscopy cameras were employed to collect data from eight ex-vivo porcine gastrointestinal (GI)-tract organs. In total, 35 sub-datasets are provided: 18 sub-datasets for colon, 12 sub-datasets for stomach and five sub-datasets for small intestine, while four of these contain polyp-mimicking elevations carried out by an expert gastroenterologist. To exemplify the use-case, SC-SfMLearner was comprehensively benchmarked. The codes and the link for the dataset are publicly available at https://***/CapsuleEndoscope/EndoSLAM. A video demonstrating the experimental setup and procedure is available at https://***/watch?v=G_LCe0aWWdQ. Copyright © 2020, The Authors. All rights reserved.

关键词： computer vision

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