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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

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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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visualization Analysis of Learning Attention Based on Single-image Pn P Head Pose Estimation

Visualization Analysis of Learning Attention Based on Single...

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2017 2nd International Conference on Education,Sports,Arts and Management engineering(ICESAME 2017)

作者： Li Dongxing Liu Heng Chang Wenquan Xu Pengfei Luo Zuying College of Information Science and Technology Beijing Normal University Engineering Research Center of Virtual Reality and Applications Ministry of Education

Learning attention analysis of students is the important indicator of classroom teaching/learning quantitative evaluation. Owing to the fact that the head-mounted eye tracker is expensive and unsuitable to be widely used in the large-scale classroom evaluation under expenditure limitation, in this paper, we uses the Pn P(Perspective-nPoint) method to estimate student's head pose for single-image. And then we achieve visualization of learning attention. Experiments demonstrate the following advantages of our method.(1) The method limits the average head-pose estimation errors under 4.88° with Biwi database.(2) This work has implemented student learning attention visualization analyses for three typical learning cases including engagement, attention, and disregard.

关键词： Head Pose visualization Attention Analysis Classroom Evaluation

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Design of temperature compensation for silicon-sapphire pressure sensor

Design of temperature compensation for silicon-sapphire pres...

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2017 IEEE International Conference on Imaging Systems and Techniques, IST 2017

作者： Manguo, Huang Defeng, Liu Chao, Lu Zhanshe, Guo Xin, Li Advanced Sensor Technology Center AVIC Beijing Changcheng Aeronautic Measurement Control Technology Research Institute Aviation Key Laboratory of Science and Technology Special Condition Monitoring Sensor Technology Beijing China School of Instrument Science and Opto-electronics Engineering State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

ISBN: (纸本)9781538616208

In order to solve the problem that the temperature drift of the silicon-on-sapphire pressure sensor is too large to reduce the accuracy of pressure measurement at high temperature, a temperature compensation scheme based on a single chip is proposed. The temperature of the working environment is obtained by a PT100, and integrated with the output of pressure sensor to calculate the compensated pressure sensor. Problems of power supply and signal acquisition are solved by the high precision, low drift voltage and current references, programmable amplifiers and high precision ADCs on chip. According to the working characteristics of the silicon-on-sapphire pressure sensor, an efficient temperature compensation algorithm is designed, and written to the on-chip ARM processor. The experimental results show that the maximum measurement error of pressure in the temperature range of -20∼250°C and measurement range of 0∼28 MPa is 8.37%FS before compensation and reduced to 0.3%FS after compensation. The accuracy of the sensor is greatly improved by the compensation system. © 2017 IEEE.

关键词： Pressure sensors

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An efficient spatial anti-aliasing method for deferred shading

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Computer Aided Drafting,Design and Manufacturing 2017年第2期27卷 57-63页

作者： Ye Chengchun Lai Shunnan Li Sheng Peking University Shenzhen Graduate School Beijing Engineering Technology Research Center of Virtual Simulation and Visualization (Peking University) School of Electronics Engineering and Computer Science Peking University Peking University Information Technology Institute (Tianjin Binhai)

In recent years, image-based anti-aliasing techniques have been widely investigated in real-time computer graphics. Compared to traditional routine of anti-aliasing, image-based methods, which combined with deferred shading, can efficiently decouple anti-aliasing step from standard graphics pipeline. However, most of the existing image-based methods consume video memory, bandwidth and computation resources heavily. In this paper, we propose an optimized anti-aliasing method, which can efficiently reduce the multi-sampling rate by a multi-sampling mask. We carefully designed the rendering system to avoid serial execution on branch divergence, by utilizing both modern GPU’s features and latest shader model. Experimental results demonstrate that our method can achieve high-quality synthesized image with real-time performance.

关键词： anti-aliasing deferred shading compute shader execution divergence

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A Parallel Markov Cerebrovascular Segmentation Algorithm Based on Statistical Model

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Journal of Computer Science & technology 2016年第2期31卷 400-416页

作者： Rong-Fei Cao Xing-Ce Wang Zhong-Ke Wu Ming-Quan Zhou Xin-Yu Liu Department of Information Science and Technology Beijing Normal University Beijing 100875 China Engineering Research Center of Virtual Reality and Applications Ministry of Education Beijing 100875 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

For segmenting cerebral blood vessels from the time-of-flight magnetic resonance angiography （TOF-MRA） images accurately, we propose a parallel segmentation algorithm based on statistical model with Markov random field （MRF）. Firstly, we improve traditional non-local means filter with patch-based Fourier transformation to preprocess the TOF-MRA images. In this step, we mainly utilize the sparseness and self-similarity of the MRA brain images sequence. Secondly, we add the MRF information to the finite mixture mode （FMM） to fit the intensity distribution of medical images. We make use of the MRF in image sequence to estimate the proportion of cerebral tissues. Finally, we choose the particle swarm optimization （PSO） algorithm to parallelize the parameter estimation of FMM. A large number of experiments verify the high accuracy and robustness of our approach especially for narrow vessels. The work will offer significant assistance for physicians on the prevention and diagnosis of cerebrovascular diseases.

关键词： cerebrovascular segmentation non-local means filtering Markov random field particle swarm optimization algorithm

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R3-Net: A deep network for multi-oriented vehicle detection in aerial images and videos

arXiv

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arXiv 2018年

作者： Li, Qingpeng Mou, Lichao Xu, Qizhi Zhang, Yun Zhu, Xiao Xiang State Key Laboratory of Virtual Reality Technology and Systems Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing100191 China Remote Sensing Technology Institute German Aerospace Center Wessling82234 Germany Signal Processing in Earth Observation Technical University of Munich Munich80333 Germany Canada Research Chair Laboratory in Advanced Geomatics Image Processing Department of Geodesy and Geomatics Engineering University of New Brunswick FrederictonNBE3B 5A3 Canada

Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called rotatable region-based residual network (R3-Net), to detect multi-oriented vehicles in aerial images and videos. More specially, R3-Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor (BAR anchor) strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In RDN, a novel rotatable position sensitive pooling (R-PS pooling) is designed to keep the position and orientation information simultaneously while downsampling the feature maps of RRoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image datasets, namely DLR 3K Munich Dataset and VEDAI Dataset, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://***/xCYD-tYudN0. Copyright © 2018, The Authors. All rights reserved.

关键词： Remote sensing

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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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Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge

arXiv

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arXiv 2019年

作者： Zhuang, Xiahai Li, Lei Payer, Christian Štern, Darko Urschler, Martin Heinrich, Mattias P. Oster, Julien Wang, Chunliang Smedby, Örjan Bian, Cheng Yang, Xin Heng, Pheng-Ann Mortazi, Aliasghar Bagci, Ulas Yang, Guanyu Sun, Chenchen Galisot, Gaetan Ramel, Jean-Yves Brouard, Thierry Tong, Qianqian Si, Weixin Liao, Xiangyun Zeng, Guodong Shi, Zenglin Zheng, Guoyan Wang, Chengjia MacGillivray, Tom Newby, David Rhode, Kawal Ourselin, Sebastien Mohiaddin, Raad Keegan, Jennifer Firmin, David Yang, Guang School of Data Science Fudan University Shanghai200433 China Fudan-Xinzailing Joint Research Center for Big Data Fudan University Shanghai200433 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Ludwig Boltzmann Institute for Clinical Forensic Imaging Graz8010 Austria Institute of Medical Informatics University of Lubeck Lubeck23562 Germany Inserm Université de Lorraine U1254 IADI Nancy France School for Technology and Health KTH Royal Institute of Technology StockholmSE-10044 Sweden School of Biomed. Eng. Health Science Centre Shenzhen University Shenzhen518060 China Dept. of Comp. Sci. and Eng. Chinese University of Hong Kong Hong Kong China University of Central Florida Orlando32816 United States School of Computer Science and Engineering Southeast University Nanjing210096 China Université de Tours 64 avenue Jean Portalis Tours37200 France School of Computer Science Wuhan University Wuhan430072 China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology SIAT Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute for Surgical Technology & Biomechanics University of Bern Bern3014 Switzerland BHF Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom Edinburgh Imaging Facility QMRI University of Edinburgh Edinburgh United Kingdom Department of Imaging Sciences & Biomedical Engineering Kings College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom National Heart and Lung Institute Imperial College London London LondonSW7 2AZ United Kingdom

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (***/zhuangxiahai/0/mmwhs/). Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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A Rapid Modeling Method for 3D Architectural Scene

A Rapid Modeling Method for 3D Architectural Scene

引用

International Conference on Cyberworlds

作者： Pu Ren Zhe Wang Yachun Fan Mingquan Zhou Guoguang Du Engineering Research Center for Virtual Reality Application MOE Beijing China College of Information Science and Technology Beijing Normal University Beijing China

The existing 3D scene layout methods, which mainly focus on indoor scenes, are limited in outdoor applications. In this paper, the example-based modeling method is introduced into the outdoor modeling, and an automatic layout optimization method for outdoor scenes is proposed. As an application platform, the sketch-based 3D model retrieval and assemble system is realized as well. Different from the current methods, firstly, we adopt an improved manifold sorting algorithm in sketch retrieval method, which can get the 3D models rapidly; secondly, according to particular properties of outdoor architectures, specialized energy constraints are proposed, which defines the energy function that meets the functional and aesthetic needs; thirdly, the scene achieves automatic layout by adopting simulated annealing algorithm. By stepped asymptotic optimization strategy and random jumps, we can avoid falling into constraint conflicts and local optimal traps. Experimental results show the robustness and effectiveness of our algorithm in different scenes. Our algorithm has been applied in the actual development of game scenes.

关键词： Solid modeling Three-dimensional displays Feature extraction Layout Manifolds Sorting Shape

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