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Pacific (formerly Asia-Pacific APVIS) visualization Symposium

作者： Can Liu Yun Han Ruike Jiang Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education) and School of EECS Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Application Peking University Beijing China Beijing Engineering Technology Research Center of Virtual Simulation and Visualization Peking University Beijing China

We propose an automatic pipeline to generate visualization with annotations to answer natural-language questions raised by the public on tabular data. With a pre-trained language representation model, the input natural language questions and table headers are first encoded into vectors. According to these vectors, a multi-task end-to-end deep neural network extracts related data areas and corresponding aggregation type. We present the result with carefully designed visualization and annotations for different attribute types and tasks. We conducted a comparison experiment with state-of-the-art works and the best commercial tools. The results show that our method outperforms those works with higher accuracy and more effective visualization.

关键词： Annotations Natural languages Pipelines Neural networks Data visualization Tools Data mining

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MultiAR: A Multi-User Augmented reality Platform for Biomedical Education

MultiAR: A Multi-User Augmented Reality Platform for Biomedi...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Markus Perz Gijs Luijten Jens Kleesiek Dieter Schmalstieg Jan Egger Christina Gsaxner Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Institute for Artificial Intelligence in Medicine University Medicine Essen Essen Germany Cancer Research Center Cologne Essen West German Cancer Center Essen Germany Institute for Visualization and Interactive Systems University of Stuttgart Stuttgart Germany Virtual and Extended Reality in Medicine (ZvRM) University Hospital Essen Essen Germany

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This paper addresses the growing integration of Augmented reality (AR) in biomedical sciences, emphasizing collaborative learning experiences. We present MultiAR, a versatile, domain-specific platform enabling multi-user interactions in AR for biomedical education. Unlike platform-specific solutions, MultiAR supports various AR devices, including handheld and head-mounted options. The framework extends across domains, augmenting biomedical education applications with collaborative capabilities. We define essential requirements for a multi-user AR framework in education, detail MultiAR’s design and implementation, and comprehensively evaluate it using anatomy education examples. Quantitative and qualitative analyses, covering system performance, accuracy metrics, and a user study with 20 participants, highlight the urgent need for a tailored collaborative AR platform in biomedical education. Results underscore enthusiasm for collaborative AR technology, endorsing MultiAR as an accessible, versatile solution for developers and end-users in biomedical education.

关键词： Measurement Accuracy Federated learning System performance Education Collaboration Anatomy engineering in medicine and biology Augmented reality

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Design of Gesture Recognition System Based on Multi-Channel Myoelectricity Correlation

Design of Gesture Recognition System Based on Multi-Channel ...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Di Wu Huiyong Li Xuefeng Liu Jianwei Niu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Hangzhou Innovation Research Institute Beihang University

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Gesture recognition systems based on myoelectric signal have raised more and more attention from researchers. Traditional gesture recognition methods are susceptible to multiple types of noise and require a large number of features, which increase overhead and decrease recognition efficiency. Fully utilizing the characteristics of the signal to recognize gestures is a big challenge. This paper proposes an improved empirical mode decomposition method (XB-EMD) based on autocorrelation function to denoise myoelectric signal. In addition, a novel deep neural network (CRNet) which combines the CNN and RNN layers together is trained for classifying the gestures based on denoised myoelectric signal. Experimental results show that the proposed gesture recognition system can improve recognition effectiveness at 97.4\% with 10 typical gestures, and 72.55\% with 16 complicated gestures.

关键词： Correlation Gesture recognition Feature extraction Empirical mode decomposition Recurrent neural networks Sensors

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Face sketch synthesis using non-local means and patch-based seaming

Face sketch synthesis using non-local means and patch-based ...

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IEEE International Conference on Image Processing

作者： Liang Chang Yves Rozenholc Xiaoming Deng Fuqing Duan Mingquan Zhou Ministry of Education Engineering Research Center of Virtual Reality and Applications China College of Information Science and Technology Beijing Normal University 3MAP5 - UMR 8145 Université Paris Descartes France Chinese Academy of Sciences Institute of Software Beijing China

ISBN: (纸本)9781479983407

This paper proposed a face sketch synthesis method by using non-local means (NL-Means), which takes the advantage of the non-local self-similarity of face photo and sketch patches. With a learning database of individuals described by one face photo and one face sketch, we assume that, for a given individual, the NL-Means coefficient of a given face photo patch is the same as its corresponding sketch patch. In order to handle the visible seam due to intensity difference of neighbor overlapping patches, we use patch based optimal seam to enforce the consistency of synthesized overlapping sketch patches. Experimental results on CUHK Face Sketch Database illustrate that our method has the advantage of easy implementation and much less required training samples, meanwhile our method can achieve fairly competitive synthesis results.

关键词： Face Training Silicon Databases Feature extraction Context Markov processes

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Visual Analysis for Wildlife Preserve based on Muti-systems

Visual Analysis for Wildlife Preserve based on Muti-systems

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IEEE Symposium on Visual Analytics Science and technology (VAST)

作者： Lijing Lin Min Lu Guozheng Li Shuai Chen Chufan Lai Ruike Jiang Qiangqiang Liu Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education) and School of EECS Peking University Key Laboratory of Machine Perception (Ministry of Education) and School of EECS Peking University Beijing Beijing CN Beijing Engineering Technology Research Center of Virtual Simulation and Visualization Peking University

In the Grand Challenge of IEEE VAST Challenge 2017, we explore the systems in each mini-challenge, and combine the discoveries logically to provide a comprehensive story happened in the wildlife preserve. In this report, we present technical details for the systems, in order to discuss how to discover the events, and how to combine them to construct an overall picture considering the additional information, newsletter, in the Grand Challenge.

关键词： Trajectory Production facilities Chemicals Visual analytics Chemical sensors Sensors

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Influence of Meniscal Attachments Placement on Articular Stress Deterioration

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Chinese Journal of Biomedical engineering(English Edition) 2018年第4期27卷 144-150页

作者： YAO Jie NIU Wen-xin Cheung Jason Tak-Man ZHANG Ming FAN Yu-bo Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education School of Biological Science and Medical EngineeringBeihang University Beijing 100191 China Li Ning Sports Science Research Center Beijing 101100 China Department of Health Technology and Informatics Hong Kong Polytechnic University Hong Kong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 100191 China

Meniscal attachments provide constraint for meniscus and contribute to knee stability. However, the influence of attachments placement on articular mechanical environment remains unclear. The present study aims to characterize the stress deterioration caused by abnormal attachment locations. Different placement of medial meniscal attachments were analyzed with a three-dimensional (3D) finite element (FE) model of knee joint. The articular stress distribution under compressive loadings indicates that abnormal placement of medial meniscal attachments could cause stress deterioration in meniscus and tibia plateau, which may increase the risk of OA. The influence of PMMA relocation was more severe than that of AMMA. The anterior displacement of both AMMA and PMMA could decrease the tibial contact area and therefore weakening the meniscal function of loading transmission. The present study provides an insight into the biomechanical character of meniscal attachments and may help improving the meniscal transplantation in the future.

关键词： meniscus attachment finite element stress knee

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Learning a saliency evaluation metric using crowdsourced perceptual judgments

arXiv

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arXiv 2018年

作者： Xia, Changqun Li, Jia Su, Jinming Borji, Ali State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China International Research Institute for Multidisciplinary Science Beihang University Beijing China Center for Research in Computer Vision Computer Science Department University of Central Florida OrlandoFL United States

In the area of human fixation prediction, dozens of computational saliency models are proposed to reveal certain saliency characteristics under different assumptions and definitions. As a result, saliency model benchmarking often requires several evaluation metrics to simultaneously assess saliency models from multiple perspectives. However, most computational metrics are not designed to directly measure the perceptual similarity of saliency maps so that the evaluation results may be sometimes inconsistent with the subjective impression. To address this problem, this paper first conducts extensive subjective tests to find out how the visual similarities between saliency maps are perceived by humans. Based on the crowdsourced data collected in these tests, we conclude several key factors in assessing saliency maps and quantize the performance of existing metrics. Inspired by these factors, we propose to learn a saliency evaluation metric based on a two-stream convolutional neural network using crowdsourced perceptual judgements. Specifically, the relative saliency score of each pair from the crowdsourced data is utilized to regularize the network during the training process. By capturing the key factors shared by various subjects in comparing saliency maps, the learned metric better aligns with human perception of saliency maps, making it a good complement to the existing metrics. Experimental results validate that the learned metric can be generalized to the comparisons of saliency maps from new images, new datasets, new models and synthetic data. Due to the effectiveness of the learned metric, it also can be used to facilitate the development of new models for fixation prediction. Copyright © 2018, The Authors. All rights reserved.

关键词： Crowdsourcing

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Crowd behavior simulation with emotional contagion in unexpected multi-hazard situations

arXiv

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arXiv 2018年

作者： Xu, Mingliang Xie, Xiaozheng Lv, Pei Niu, Jianwei Wang, Hua Li, Chaochao Zhu, Ruijie Deng, Zhigang Zhou, Bing Center for Interdisciplinary Information Science Research ZhengZhou University 450000 State Key Laboratory of Virtual Reality Technology and Systems Beihang University 100091 School of Computer and Communication Engineering of Zhengzhou University of Light Industry 450000 Department of Computer Science University of Houston HoustonTX77204-3010

Numerous research efforts have been conducted to simulate crowd movements, while relatively few of them are specifically focused on multi-hazard situations. In this paper, we propose a novel crowd simulation method by modeling the generation and contagion of panic emotion under multi-hazard circumstances. In order to depict the effect from hazards and other agents to crowd movement, we first classify hazards into different types (transient and persistent, concurrent and non-concurrent, static and dynamic) based on their inherent characteristics. Second, we introduce the concept of perilous field for each hazard and further transform the critical level of the field to its invoked-panic emotion. After that, we propose an emotional contagion model to simulate the evolving process of panic emotion caused by multiple hazards. Finally, we introduce an Emotional Reciprocal Velocity Obstacles (ERVO) model to simulate the crowd behaviors by augmenting the traditional RVO model with emotional contagion, which for the first time combines the emotional impact and local avoidance together. Our experiment results demonstrate that the overall approach is robust, can better generate realistic crowds and the panic emotion dynamics in a crowd. Furthermore, it is recommended that our method can be applied to various complex multi-hazard environments. Copyright © 2018, The Authors. All rights reserved.

关键词： Hazards

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How drones look: Crowdsourced knowledge transfer for aerial video saliency prediction

arXiv

引用

arXiv 2018年

作者： Fu, Kui Li, Jia Shen, Hongze Tian, Yonghong State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China International Research Institute for Multidisciplinary Science Beihang University Beijing100191 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Cooperative Medianet Innovation Center China

In ground-level platforms, many saliency models have been developed to perceive the visual world as the human does. However, they may not fit a drone that can look from many abnormal viewpoints. To address this problem, this paper proposes a Crowdsourced Multi-path Network (CMNet) that transfer the ground-level knowledge for spatiotemporal saliency prediction in aerial videos. To train CMNet, we first collect and fuse the eye-tracking data of 24 subjects on 1,000 aerial videos to annotate the ground-truth salient regions. Inspired by the crowdsourced annotation in eye-tracking experiments, we design a multi-path architecture for CMNet, in which each path is initialized under the supervision of a classic ground-level saliency model. After that, the most representative paths are selected in a data-driven manner, which are then fused and simultaneously fine-tuned on aerial videos. In this manner, the prior knowledge in various classic ground-level saliency models can be transferred into CMNet so as to improve its capability in processing aerial videos. Finally, the spatial predictions given by CMNet are adaptively refined by incorporating the temporal saliency predictions via a spatiotemporal saliency optimization algorithm. Experimental results show that the proposed approach outperforms ten state-of-the-art models in predicting visual saliency in aerial videos. Copyright © 2018, The Authors. All rights reserved.

关键词： Crowdsourcing

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MemNetAR: Memory Network with Adversative Relation for Target-Level Sentiment Classification

MemNetAR: Memory Network with Adversative Relation for Targe...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Yiwei Gao Jianwei Niu Xuefeng Liu Kaili Mao Shui Yu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Hangzhou Innovation Research Institute Beihang University School of Computer Science and Cyber Engineering Guangzhou University Guangdong China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Target-level sentiment classification aims to identify the sentiment of multiple targets in a sentence. Although existing approaches based on neural network have achieved good performance in this task, we find that many approaches tend to give the same predictions for instances that have multiple targets, and this tendency can make a low accuracy for those instances that have different classes for different targets. Based on this observation, we propose MemNetAR, a memory network which can explicitly leverage the adversative relation among multiple targets in a sentence. Specifically, we add an adversative loss to the cross-entropy loss when there are adversative words between targets. The experimental results on public laptop and restaurant datasets prove that our model can improve 0.84% and 0.8% on total test dataset, and improve 2.97% and 2.68% on the dataset consisting of those instances with multiple targets but different classes by leveraging this new adversative information.

关键词： Task analysis Feature extraction Sentiment analysis Portable computers Machine learning Training Predictive models

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