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arXiv 2019年

作者： Li, Xiaomeng Yu, Lequan Fu, Chi-Wing Fang, Meng Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Tencent AI Lab United States

The goal of few-shot learning is to recognize new visual concepts with just a few amount of labeled samples in each class. Recent effective metric-based few-shot approaches employ neural networks to learn a feature similarity comparison between query and support examples. However, the importance of feature embedding, i.e., exploring the relationship among training samples, is neglected. In this work, we present a simple yet powerful baseline for few-shot classification by emphasizing the importance of feature embedding. Specifically, we revisit the classical triplet network from deep metric learning, and extend it into a deep K-tuplet network for few-shot learning, utilizing the relationship among the input samples to learn a general representation learning via episode-training. Once trained, our network is able to extract discriminative features for unseen novel categories and can be seamlessly incorporated with a non-linear distance metric function to facilitate the few-shot classification. Our result on the miniImageNet benchmark outperforms other metric-based few-shot classification methods. More importantly, when evaluated on completely different datasets (Caltech-101, CUB-200, Stanford Dogs and Cars) using the model trained with miniImageNet, our method significantly outperforms prior methods, demonstrating its superior capability to generalize to unseen classes. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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Scenario Design Tool Based on Hybrid Input

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Ruan Jian Xue Bao/Journal of Software 2019年 30卷 48-61页

作者： Tong, Qing-Shan Zhang, Zong-Qi Huang, Jin Tian, Feng Liu, Jie Dai, Guo-Zhong Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100190 China

Pen-based user interface relying on touch technology is one of the Post-WIMP interfaces. It discards the physical keyboard and mouse, which changed the method of human-computer interaction to some extent. Though sketch drawing software and recognition software are constantly emerging, there is no mature pen-based interface design development tool. Based on the PGIS interaction paradigm and scenario design method, this paper develops a tool named with SDT that allows hybrid input of graphics and sketchs based on pen-based interaction primitives. Firstly, based on the principle of high-cohesion and low-coupling in the field of software engineering, the "Separation-Fusion" design method is proposed. Accordingly, the overall architecture of the system is put forward. Secondly, the essential technologies are elaborated from three aspects: user interface description language, pen-based interactive primitive and mono-case, hybrid input. Thirdly, an example of a complete application is built by the SDT, which makes the availability and feasibility of the system more convincible. Finally, the advantage and effectiveness of the tool are verified by two evaluation experiments. © Copyright 2019, Institute of Software, the Chinese Academy of sciences. All rights reserved.

关键词： human computer interaction

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Tilting-Twisting-Rolling: a pen-based technique for compass geometric construction

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science China(Information sciences) 2017年第5期60卷 246-251页

作者： Fei LYU Feng TIAN Guozhong DAI Hongan WANG State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences School of Digital Media and Design Arts Beijing University of Posts and Telecommunications Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences

This paper presents a new pen-based technique, Tilting-Twisting-Rolling, to support compass geometric construction. By leveraging the 3D orientation information and 3D rotation information of a pen, this technique allows smooth pen action to complete multi-step geometric construction without switching task *** from a user study show this Tilting-Twisting-Rolling technique can improve user performance and user experience in compass geometric construction.

关键词： Tilting-Twisting-Rolling pen-based tool virtual compass geometry construction interaction

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Non-Frontal Facial Expression Recognition Using a Depth-Patch Based Deep Neural Network

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Journal of computer science & Technology 2017年第6期32卷 1172-1185页

作者： Nai-Ming Yao Hui Chen Qing-Pei Guo Hong-An Wang Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Bcijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China

The challenge of coping with non-frontal head poses during facial expression recognition results in considerable reduction of accuracy and robustness when capturing expressions that occur during natural communications. In this paper, we attempt to recognize facial expressions under poses with large rotation angles from 2D videos. A depth^patch based 4D expression representation model is proposed. It was reconstructed from 2D dynamic images for delineating continuous spatial changes and temporal context under non-frontal cases. Furthermore, we present an effective deep neural network classifier, which can accurately capture pose-variant expression features from the depth patches and recognize non-frontal expressions. Experimental results on the BU-4DFE database show that the proposed method achieves a high recognition accuracy of 86.87% for non-frontal facial expressions within a range of head rotation angle of up to 52%, outperforming existing methods. We also present a quantitative analysis of the components contributing to the performance gain through tests on the BU-4DFE and Multi-PIE datasets.

关键词： facial expression recognition non-frontal head pose depth spatial-temporal convolutional neural network

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Revisiting Shadow Detection: A New Benchmark Dataset for Complex World

arXiv

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arXiv 2019年

作者： Hu, Xiaowei Wang, Tianyu Fu, Chi-Wing Jiang, Yitong Wang, Qiong Heng, Pheng-Ann The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world. Though recent shadow detectors have already achieved remarkable performance on various benchmark data, their performance is still limited for general real-world situations. In this work, we collected shadow images for multiple scenarios and compiled a new dataset of 10,500 shadow images, each with labeled ground-truth mask, for supporting shadow detection in the complex world. Our dataset covers a rich variety of scene categories, with diverse shadow sizes, locations, contrasts, and types. Further, we comprehensively analyze the complexity of the dataset, present a fast shadow detection network with a detail enhancement module to harvest shadow details, and demonstrate the effectiveness of our method to detect shadows in general situations. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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A Scalable Deep Attention Mechanism of Instance Segmentation for the Investigation of Chromosome

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SLAS technology 2025年 33卷 100306页

作者： Neelam Umbreen Sara Ali Hasan Sajid Yasar Ayaz Shrooq Alsenan Yunyoung Nam So Yeon Kim Muhammad Baber Sial National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: neelamumbreen.phd@smme.edu.pk. National University of Sciences and Technology (NUST) Islamabad Pakistan Suzhou Industrial Park (SIP) Monash Research Institute of Science and Technology Monash University 8Q9C+R6W Suzhou Jiangsu China. School of Mechanical and Manufacturing Engineering (SMME) National University of Sciences and Technology (NUST) Islamabad NUST-COVENTRY Human-Robot Interaction (NC-HRI) Laboratory Pakistan Intelligent Field Robotics Laboratory (IFRL) National Center for Artificial Intelligence (NCAI) National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: sarababer@smme.nust.edu.pk. National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: hasan.sajid@smme.nust.edu.pk. Intelligent Field Robotics Laboratory (IFRL) National Center for Artificial Intelligence (NCAI) National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: yasar@smme.nust.edu.pk. Information Systems Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia. Electronic address: shalsenan@pnu.edu.sa. Emotional and Intelligent Child Care Convergence Research Center Soonchunhyang University Asan 31538 Korea. Electronic address: ynam@sch.ac.kr. Department of Artificial Intelligence Department of Software and Computer Engineering Ajou University. Electronic address: jebi1771@ajou.ac.kr. School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China. Electronic address: babersial@***.

Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated datasets. This study proposes a robust instance segmentation framework that integrates an automated annotation pipeline with an enhanced deep learning architecture to address these challenges. A novel dataset is introduced, comprising metaphase images and corresponding karyograms, annotated with precise instance segmentation information across 24 chromosome classes in COCO format. To overcome the labor-intensive manual annotation process, a feature-based image registration technique leveraging SIFT and homography is employed, enabling the accurate mapping of chromosomes from karyograms to metaphase images and significantly improving annotation quality and segmentation performance. The proposed framework includes a custom Mask R-CNN model enhanced with an Attention-based Feature Pyramid Network (AttFPN), spatial attention mechanisms, and a LastLevelMaxPool block for superior multi-scale feature extraction and focused attention on critical regions of the image. Experimental evaluations demonstrate the model's efficacy, achieving a mean average precision (mAP) of 0.579 at IoU = 0.50:0.95, surpassing the baseline Mask R-CNN and Mask R-CNN with AttFPN by 3.94% and 5.97% improvements in mAP and AP50, respectively. Notably, the proposed architecture excels in segmenting small and medium-sized chromosomes, addressing key limitations of existing methods. This research not only introduces a state-of-the-art segmentation framework but also provides a benchmark dataset, setting a new standard for chromosome instance segmentation in biomedical imaging. The integration of automated dataset creation with advanced model design offers a scalable and transferable solution, paving the way for tackling similar challenges in other domains of biomedical and cytogenetic imagi

关键词： Chromosome segmentation Instance segmentation, cytogenetics Mask R-CNN attention mechanism biomedical imaging multi-scale feature fusion

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AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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Correction to: Editorial

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International Journal of Social Robotics 2020年第1期12卷 297-297页

作者： Agnieszka Wykowska Shuzhi Sam Ge Oussama Khatib Social Cognition in Human-Robot Interaction Istituto Italiano Di Tecnologia Genoa Italy Social Robotics Laboratory Interactive Digital Media Institute The National University of Singapore Singapore Singapore Centre for Robotics UESTC Chengdu China Robotics Laboratory Department of Computer Science Stanford University Stanford USA

The following mistakes were inadvertently overlooked in the production of the Editorial of the January issue of Social Robotics.

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Design, Development and Control of a Three Flexible-Fingers Gripper Based On Hand Gesture

Design, Development and Control of a Three Flexible-Fingers ...

引用

RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Rasoul Sadeghian Pooyan Sedigh Parastoo Azizinezhad Shahrooz Shahin Mehdi Tale Masouleh Human and Robot Interaction Laboratory Tehran Iran Department of Biomedical Engineering Science and Research Branch Islamic Azad University Tehran Iran Faculty of Electrical and Computer Science Robotic Engineering Tehran North Branch Islamic Azad University Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781728101286;9781728101279

In this paper, the process of design, and construction of a flexible gripper with three fingers is presented. The fingers of the proposed gripper are fabricated using a 3D printing device. The gripper is controlled remotely by an autonomous glove which is designed to help people with disability. In order to ameliorate the movement accuracy of the gripper a Fuzzy-PID controller is implemented on the system. The result of experimental tests demonstrate a decrease of the gripper movement error to less than 2 degrees in average. An ultrasonic sensor is set on the gripper's body to improve the grasping process by detecting the distance of the object to the gripper. The fabricated fingers are flexible and provides the ability of grasping the objects with irregular shapes. The experimental test approves the significant performance of the gripper in term of grasping complex shape objects.

关键词： Grippers Grasping Flexible printed circuits Robot sensing systems Shape Kalman filters

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Compare Variation of User’s EEG Owing to Parallax Cue

Compare Variation of User’s EEG Owing to Parallax Cue

引用

International Conference on Display Technology, ICDT 2018

作者： Hu, Haichen Wang, Danli Yue, Kang Yang, Xinpan Chiu, Steve C. The State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China College of Science and Engineering Idaho State University Pocatello United States

ISBN: (纸本)9781510867659

3D display has become increasingly popular in many fields. It can provide incredible immersion compared with 2D display. There are many researches referring to the change of user about no matter subjective experience or objective physiology signal while watching 3D content. Similarly, in this paper, we study the variation of user’s EEG when the user watched 3D content. However, we only focus on parallax cue and avoid other cues of 3D content by using Random Dot Stereogram(RDS) as visual stimulus. We designed an experiment which asked the subject to watch RDS with nine kinds of parallaxes(0,±0.5,±1.1,±1.3,±1.7). The users watched one RDS for 1 minute and then closed eyes and rest for 1 minute every time. During watching, EEG signal with 30-channel was recorded. The result suggests that significant variation of the user’s EEG data occurred in the Alpha(8-13Hz) and Beta(14-30Hz) frequency band by different parallax’s comparison. And we found not only parallax but also electrode position can impact the user’s EEG data significantly. Accurately, the EEG data has a significant change in six electrodes position in both frequency bands including CZ 、 PZ 、 OZ 、 O2 in international 10-20 system. It means those electrode positions we detect can be regarded as typical positions where we record EEG signal. It will simplify the process of EEG signal acquisition when we need to study the relationship between parallax cue of 3D display and the user further. © 2018 SID.

关键词： Three dimensional displays

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