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

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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

Haptics-equiped interactive PCI simulation for patient-specific surgery training and rehearsing

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Science China(Information Sciences) 2016年第10期59卷 233-238页

作者： Shuai LI Qing XIA Aimin HAO Hong QIN Qinping ZHAO State Key Laboratory of Virtual Reality Technology and Systems Beihang University Department of Computer Science Stony Brook University

Despite the long history of medical simulations, suffering from the patient-specific heterogeneous heart physiological structure and complex intravascular procedures, it is still challenging for patient-specific percutaneous coronary intervention(PCI) surgery simulation. In this paper, we advocate a haptics-equiped interactive prototype system towards PCI surgeons training and patient-specific surgery rehearsing, which can afford trainees the opportunity to approximately experience the entire PCI procedures and customized emergency cases that might occur in common clinical settings. The full simulation covers tissue deformation, catheter and wire simulation, X-ray simulation, haptic feedback, and 3D realistic rendering, which in all give rise to the integrated physical, visual, haptic, and procedural realism. Our system can accommodate various comprehensive operations involved in PCI-related procedures, including feeding wires, releasing stents, injecting contrast medium, simulating X-ray, bleeding, etc. Moreover, our system framework is fully built upon CUDA, and thus can achieve real-time performance even on a common desktop. The high-fidelity, real-time efficiency and stableness of our system show great potentials for its practical applications in clinical training fields.

关键词： percutaneous coronary intervention(PCI) PCI surgery simulation patient-specific surgery rehearsing physically-based modeling haptics

Research on the influence factors of consumer repurchase in Dutch auction

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Research on the influence factors of consumer repurchase in ...

2018中国信息通信大会(CICC2018)

作者： JIANG Wei-Jin JIANG Yi-Rong CHEN Jia-Hui XU Yu-Hui WANG Yang TAN Li-Na School of Computer Science and Technology Wuhan University of Technology Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Institute of Big Data and Internet Innovation Mobile E-business Collaborative Innovation Center of Hunan Province Hunan University of Commerce Tonghua Normal University

With the rapid development of e-commerce,the online shopping market becomes more and more *** to design sales models to retain consumers and make them become continuous buying is the biggest concern of most e-commerce *** study was under the situation of Dutch auction model,using a large number of real auction data in Gongtianxia’s "7 days auction" and "15 minutes auction".We use quantitative research method combining statistical analysis and panel data regression model,to study the influencing factors of consumers’ repeat *** results show that auction methods,consumers’ perceived performance and bidding structure can affect consumers *** the same time,we established random effects regression models of the rate of consumers repurchase in 7 days and 30 *** to the conclusion of the research in this article,we can provide some practical suggestions for Gongtianxia and a reference for other e-commerce.

关键词： Dutch Auction Continuous purchases Panel data Random effects model

LEARNING FROM WEAKLY-LABELED CLINICAL DATA FOR AUTOMATIC THYROID NODULE CLASSIFICATION IN ULTRASOUND IMAGES

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LEARNING FROM WEAKLY-LABELED CLINICAL DATA FOR AUTOMATIC THY...

IEEE International Conference on Image Processing

作者： Jianxiong Wang Shuai Li Wenfeng Song Hong Qin Bo Zhang Aimin Hao State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Department of Computer Science Stony Brook University Stony Brook USA Department of Ultrasound Chinese Academy of Medical Science & Peking Union Medical College Hospital Beijing China

This paper proposes a semi-supervised learning method based on weakly-labeled data to automatically classify ultrasound (US) thyroid nodules. key to our new approach is the unification of multi-instance learning (MIL) with deep learning. Benefiting from that, our method can directly use off-the-shelf clinical data, which involves no labels to indicate nodule classes. To this end, we take the US images of a patient as a bag, and take the corresponding pathology report as the bag label. Specifically, we first propose a bag generating method, wherein the detected thyroid nodules are considered as instances corresponding to certain bag. After that, we design an effective EM algorithm to train a convolutional neural network (CNN) for nodule classification. We conduct extensive experiments and comprehensive evaluations on different datasets, and all the experiments confirm that, our method significantly outperforms state-of-the-art MIL algorithms, which exhibits great potential in clinical applications.

关键词： Cancer Proposals Image edge detection Training Biomedical imaging Feature extraction Ultrasonic imaging

Complementary Segmentation of Primary Video Objects with Reversible Flows

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arXiv 2018年

作者： Li, Jia Wu, Junjie Zheng, Anlin Song, Yafei Zhang, Yu Chen, Xiaowu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China National Engineering Laboratory for Video Technology School of EE&CS Peking University Beijing100871 China A.I. Labs Alibaba Group Beijing100102 China SenseTime Group Limited 100084 China

—Segmenting primary objects in a video is an important yet challenging problem in computer vision, as it exhibits various levels of foreground/background ambiguities. To reduce such ambiguities, we propose a novel formulation via exploiting foreground and background context as well as their complementary constraint. Under this formulation, a unified objective function is further defined to encode each cue. For implementation, we design a Complementary Segmentation Network (CSNet) with two separate branches, which can simultaneously encode the foreground and background information along with joint spatial constraints. The CSNet is trained on massive images with manually annotated salient objects in an end-to-end manner. By applying CSNet on each video frame, the spatial foreground and background maps can be initialized. To enforce temporal consistency effectively and efficiently, we divide each frame into superpixels and construct neighborhood reversible flow that reflects the most reliable temporal correspondences between superpixels in far-away frames. With such flow, the initialized foregroundness and backgroundness can be propagated along the temporal dimension so that primary video objects gradually pop-out and distractors are well suppressed. Extensive experimental results on three video datasets show that the proposed approach achieves impressive performance in comparisons with 18 state-of-the-art models. Copyright © 2018, The Authors. All rights reserved.

关键词： Motion compensation

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

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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

Automatic 3D cardiovascular MR segmentation with densely-connected volumetric convnets 1

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20th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2017

作者： Yu, Lequan Cheng, Jie-Zhi Dou, Qi Yang, Xin Chen, Hao Qin, Jing Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong Department of Electrical Engineering Chang Gung University Taoyuan Taiwan Centre for Smart Health School of Nursing The Hong Kong Polytechnic University Kowloon Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9783319661858

ISBN: (纸本)9783319661841

Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treatment of cardiovascular disease. However, this task is very challenging due to ambiguous cardiac borders and large anatomical variations among different subjects. In this paper, we propose a novel densely-connected volumetric convolutional neural network, referred as DenseVoxNet, to automatically segment the cardiac and vascular structures from 3D cardiac MR images. The DenseVoxNet adopts the 3D fully convolutional architecture for effective volume-to-volume prediction. From the learning perspective, our DenseVoxNet has three compelling advantages. First, it preserves the maximum information flow between layers by a densely-connected mechanism and hence eases the network training. Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data. Third, we add auxiliary side paths to strengthen the gradient propagation and stabilize the learning process. We demonstrate the effectiveness of DenseVoxNet by comparing it with the state-of-the-art approaches from HVSMR 2016 challenge in conjunction with MICCAI, and our network achieves the best dice coefficient. We also show that our network can achieve better performance than other 3D ConvNets but with fewer parameters. © Springer International Publishing AG 2017.

关键词： Medical applications

Deep reinforcement learning for unsupervised video summarization with diversity-representativeness reward

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arXiv 2017年

作者： Zhou, Kaiyang Qiao, Yu Xiang, Tao Guangdong Key Lab of Computer Vision and Virtual Reality Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Queen Mary University of London

Video summarization aims to facilitate large-scale video browsing by producing short, concise summaries that are diverse and representative of original videos. In this paper, we formulate video summarization as a sequential decision-making process and develop a deep summarization network (DSN) to summarize videos. DSN predicts for each video frame a probability, which indicates how likely a frame is selected, and then takes actions based on the probability distributions to select frames, forming video summaries. To train our DSN, we propose an end-to-end, reinforcement learning-based framework, where we design a novel reward function that jointly accounts for diversity and representativeness of generated summaries and does not rely on labels or user interactions at all. During training, the reward function judges how diverse and representative the generated summaries are, while DSN strives for earning higher rewards by learning to produce more diverse and more representative summaries. Since labels are not required, our method can be fully unsupervised. Extensive experiments on two benchmark datasets show that our unsupervised method not only outperforms other state-of-the-art unsupervised methods, but also is comparable to or even superior than most of published supervised approaches. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep learning

Wide Baseline Image Stitching with Structure-Preserving

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Wide Baseline Image Stitching with Structure-Preserving

International Conference on virtual reality and Visualization (ICVRV)

作者： Mingjun Cao Wei Lyu Zhong Zhou Wei Wu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781538626375;9781538626368

This paper presents a novel stitching approach for wide-baseline images with low texture. Firstly, a three-phase feature matching model is applied to extract rich and reliable feature matching, in the case of low texture, our line matching and contour matching will compensate for the poor quality of point matching. Then, a structure-preserving warping is performed, by defining several constraints and minimizing the objective function to solve the optimal mesh, with which we obtain multiple affine matrices to warp images. Furthermore, we synthetically consider alignment error, color difference and saliency difference to find the optimal seam for image blending. Experiments both on common data sets and challenging surveillance scenes illustrate the effectiveness of the proposed method, and our approach has outstanding performance when compared with other state-of-the-art methods.

关键词： Conferences virtual reality Visualization