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ASRNet: Adversarial Segmentation and Registration Networks for Multispectral Fundus Images

computer Systems Science & Engineering 2021年第3期36卷 537-549页

作者： Yanyun Jiang Yuanjie Zheng Xiaodan Sui Wanzhen Jiao Yunlong He Weikuan Jia School of Information Science and Engineering Shandong Normal UniversityJinan250358China Key Laboratory of Intelligent Computing&Information Security in Universities of Shandong Shandong Provincial Key Laboratory for Novel Distributed Computer Software TechnologyInstitute of Biomedical SciencesShandong Normal UniversityJinan250358China Shandong Provincial Hospital Affiliated to Shandong University Jinan250021China INSA Lyon University of LyonCNRSInsermVilleurbanne69621CedexFrance

Multispectral imaging (MSI) technique is often used to capture imagesof the fundus by illuminating it with different wavelengths of light. However,these images are taken at different points in time such that eyeball movementscan cause misalignment between consecutive images. The multispectral imagesequence reveals important information in the form of retinal and choroidal bloodvessel maps, which can help ophthalmologists to analyze the morphology of theseblood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deeplearning framework called “Adversarial Segmentation and Registration Nets”(ASRNet) for the simultaneous estimation of the blood vessel segmentation andthe registration of multispectral images via an adversarial learning process. ASRNet consists of two subnetworks: (i) A segmentation module S that fulfills theblood vessel segmentation task, and (ii) A registration module R that estimatesthe spatial correspondence of an image pair. Based on the segmention-drivenregistration network, we train the segmentation network using a semi-supervisedadversarial learning strategy. Our experimental results show that the proposedASRNet can achieve state-of-the-art accuracy in segmentation and registrationtasks performed with real MSI datasets.

关键词： Deep learning deformable image registration image segmentation multispectral imaging(MSI)

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Teaching Activity Design Based on the Discipline key Competence of IT in Secondary Vocational School

Teaching Activity Design Based on the Discipline Key Compete...

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International Conference on Application of Intelligent Systems in Multi-modal Information Analytics, MMIA 2020

作者： Zhang, Penglong Zhang, Yongsheng Liu, Lining Sun, Wenqian School of Information Science and Engineering Shandong Normal University ShandongJinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology ShandongJinan250358 China

ISBN: (纸本)9783030514303

This paper, is based on the analysis of secondary vocational students learning of information technology, that’s what we call IT. In response to a new round of curriculum reform emphasizes the key competence, we sum high school IT discipline key competence and vocational education characteristics up to the secondary vocational discipline key competence, and design related teaching activity to change the secondary vocational school IT course "computer application foundation". So as to improve secondary vocational students’ IT discipline key competence, and to provide the reference for school IT teaching material writing, content design and classroom teaching. © 2021, Springer Nature Switzerland AG.

关键词： Students

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Research on the Method of Emotion Contagion in Virtual Space Based on SIRS 15th

Research on the Method of Emotion Contagion in Virtual Space...

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15th CCF Conference on computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020

作者： Liu, Heng Hong, Xiao Lu, Dianjie Zhang, Guijuan Liu, Hong School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

ISBN: (纸本)9789811625398

There is emotional contagion in virtual space, and the process of emotional contagion is also affected by emotional recurrence. However, the existing emotional contagion model does not consider the impact of individual emotional recurrence on emotional contagion in the virtual space. Therefore, the emotional contagion process cannot be simulated. The paper proposes a method of recurrent emotional contagion in virtual space. First, we construct an individual emotional recurrence contagion model based on SIRS (Susceptible-Infected-Recovered-Susceptible), we propose emotional recurrence contagion rules, and consider the impact of emotional recurrence in the virtual space on the emotional contagion process;secondly, we construct a virtual space emotional recurrence contagion model (REC) and solve it to simulate the process of emotional recurrence contagion. Third, we build a WS small-world network based on REC simulation. Provide REC (Recurrent emotional contagion model) simulation process in WS small-world network to verify the rationality of the REC. Finally, the REC is simulated to show the simulation effect of emotional recurrence contagion. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Small-world networks

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A Dueling-DDPG Architecture for Mobile Robots Path Planning Based on Laser Range Findings 18th

A Dueling-DDPG Architecture for Mobile Robots Path Planning ...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Zhao, Panpan Zheng, Jinfang Zhou, Qinglin Lyu, Chen Lyu, Lei School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China

ISBN: (纸本)9783030891879

Planning an obstacle-free optimal path presents great challenges for mobile robot applications, the deep deterministic policy gradient (DDPG) algorithm offers an effective solution. However, when the original DDPG is applied to robot path planning, there remains many problems such as inefficient learning and slow convergence that can adversely affect the ability to acquire optimal path. In response to these concerns, we propose an innovative framework named dueling deep deterministic policy gradient (D-DDPG) in this paper. First of all, we integrate the dueling network into the critic network to improve the estimation accuracy of Q-value. Furthermore, we design a novel reward function by combining the cosine distance with the Euclidean distance to improve learning efficiency. Our proposed model is validated by several experiments conducted in the simulation platform Gazebo. Experiments results demonstrate that our proposed model has the better path planning capability even in the unknown environment. © 2021, Springer Nature Switzerland AG.

关键词： Robot applications

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GCMNet: Gated Cascade Multi-scale Network for Crowd Counting 18th

GCMNet: Gated Cascade Multi-scale Network for Crowd Counting

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Zheng, Jinfang Zhao, Panpan Xie, Jinyang Lyu, Chen Lyu, Lei School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China

ISBN: (纸本)9783030893620

With the rapid development of convolutional neural networks, many CNN-based methods have emerged and made promising progress in the field of crowd counting. However, dealing with extremely scale variation remains a challenging but attractive issue. In this paper, we propose an innovative Gated Cascade Multi-scale Network (GCMNet) to tackle with the issue by taking full advantage of the representation of multi-scale features in a multi-level network. First of all, we implement such an idea by obtaining rich contextual information with a multi-scale contextual information enhancement module. Then, considering the pixel-level image detail information that is lost during the successive feature extraction process, we propose a hopping cascade module to refine this detail information. However, naively refining all the detail information is sub-optimal. Therefore, a gated information selection delivery module is designed to adaptively control the delivery of information between multi-level features. Combined with our proposed module, our method can effectively generate high-quality crowd density maps. The superiority of our method over current methods is demonstrated through extensive experiments on four challenging datasets. © 2021, Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Singular Value Decomposition Based Pedestrian Trajectory Prediction

Singular Value Decomposition Based Pedestrian Trajectory Pre...

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International Conference on computer Supported Cooperative Work in Design

作者： Heng Wang Guijuan Zhang Hong Liu Dianjie Lu School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

Numerous cameras deployed in venues can collect video data, which can be analyzed to help evacuate people in emergency situations. Most surveillance videos can provide data support for pedestrian trajectory prediction. However, data-driven prediction method do not consider the impact of personalization on pedestrian trajectories, which results in ignoring individual personalization. How to accurately analyze the differences among pedestrians and yet accurately predict pedestrian trajectories is a challenging problem. To solve the above problems, we propose an singular value decomposition (SVD) based pedestrian trajectory prediction method, which introduces matrix decomposition to pedestrian trajectory prediction for the first time. Thus, This method analyzes the impact of pedestrian personalization on motion trajectories by mining the interaction patterns between pedestrians and environment. Firstly, we propose an information collection method based on environmental semantics, which can extract scene information from historical video data to construct an environmental information matrix. Secondly, we propose an SVD-based individual environmental feature preference method, which uses singular value decomposition methods to mine the data and analyze personalized pedestrian motion patterns to construct a personalized individual preference matrix. Finally, we built an personalized trajectory prediction method to predict pedestrian movement trajectories. The experimental results show that the method can not only analyze the effect of personalization on pedestrian movement but also accurately predict the pedestrian movement trajectory.

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Risk Region-based Prediction Model for the Epidemic Spreading

Risk Region-based Prediction Model for the Epidemic Spreadin...

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International Conference on computer Supported Cooperative Work in Design

作者： Fuwei Li Dianjie Lu Baoxu An Guijuan Zhang School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

Studying the spread process of epidemics in the crowd is a significant topic, which can help to take measures in advance to avoid epidemic spread and reduce the losses of life and property of the general public. However, this is a challenging problem that how to capture the spatio-temporal characteristics of the epidemic spreading. In this paper, we propose a risk region-based prediction model to characterize the spread feature. First, we propose a risk region-based spread model to describe the spatial relationship between individuals by dividing risk regions and assigning different spread characteristics to different risk regions. Second, we develop a spatio-temporal point process-based infection intensity quantification method to describe the variation of epidemic spread characteristics over time in different risk regions in the form of intensity. Then, we propose a long short-term memory (LSTM) based intensity function prediction method to solve the corresponding intensity and predict the spread process by the spatio-temporal point process (STPP). Finally, we implement an epidemic spreading simulation platform to verify our method and visualize the experimental results. The proposed model is expected to provide guidance for predicting the spread of epidemics.

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Learners’ States Monitoring Method Based on Face Recognition technology

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Procedia computer Science 2022年 202卷 172-177页

作者： Xiaoyi Qiao Xiangwei Zheng Xiaofang Sun Shuqin Li Yuang Zhang School of Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan 250014 China

At present, many scholars focus on the learners’ states in online learning, however, a complete real-time monitoring system of learners’ states has not been proposed. In order to better detect the learning process and provide learners with a basis for self-reflection, this paper describes a learners’ states monitoring method based on face recognition technology (LSMFR). A learner monitoring system is developed to collect the learners’ states in real-time through external cameras and record the tired blinking, yawning, head posture, and emotional expression of learners respectively. After a comprehensive analysis, the evaluation score of learners’ states is obtained. In the running process of the system, information feedback is completed by recording the detection content in real-time and then states of learning effectiveness are generated. After learning for a short time, learners check the data record for self-reflection. The simulation test is carried out on the system and the analysis shows that the system achieves the correct analysis of the learners’ states, and generates accurate evaluation scores.

关键词： face recognition real-time monitoring procedure analysis learners’ states

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Deep reinforcement learning-based panic crowd evacuation simulation

Deep reinforcement learning-based panic crowd evacuation sim...

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International Conference on computer Supported Cooperative Work in Design

作者： Baoxu An Guijuan Zhang Fuwei Li Nianyun Song Hong Liu Dianjie Lu School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

Crowd evacuation simulation can provide guidance for public emergencies or mass casualties which is easy to cause crowding and stampede. Deep reinforcement learning is an effective method for the path planning of crowd evacuation simulation which can reduce the dependence on data and has strong generalization. However, current methods of deep reinforcement learning ignore the consideration of panic emotion which could result in weak authenticity since the panic emotion has a significant impact on crowd evacuation. To address this problem, we propose a novel deep reinforcement learning for the panic crowd evacuation. First, a quadrant based crowd network is constructed to guide the movement of agents according to the change of panic degree in different quadrants. Second, a method of quantifying panic degree is proposed. Through the mean field equation, the dynamic evolution of agent states is realized, and the panic degree in different regions is quantified. Finally, a panic crowd evacuation model based on multi-agent deep deterministic policy gradient (MADDPG) was established. The computated panic degree is introduced into the reward function to recommend a path for multiagent to avoid panic. Experimental results show the effectiveness of our method.

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Spatial-temporal Consistency based Crowd Emotion Recognition

Spatial-temporal Consistency based Crowd Emotion Recognition

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International Conference on computer Supported Cooperative Work in Design

作者： Chuanmiao Zhao Guijuan Zhang Lei Lv Hong Liu Dianjie Lu School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

In crowd evacuation scenarios, it is an effective way to ensure evacuation safety by recognizing real crowd emotions and then taking measures such as emotional infection to reduce crowd panic. However, factors such as distance, exposure, angle, and occlusion can lead to incomplete face expression information collection, thus failing to accurately identify crowd emotions. Therefore, it is still a very challenging problem to recognize individual emotions in the case of incomplete face expression information collection, and thus accurately identify crowd emotions during evacuation. To solve this problem, we propose a spatial-temporal consistency-based crowd emotion recognition method to accurately identify the real emotions of the crowd. First, for video frames that can capture the complete facial expression information, we use the residual network to accurately identify the individual emotion values in each frame. For video frames that cannot capture the complete facial expression information, we propose an individual emotion calculation model based on spatial-temporal consistency to calculate the individual emotion values in each frame. Second, we define the crowd panic level and obtain the real crowd emotion by calculation. Finally, we implement an end-to-end crowd panic emotion recognition system to verify our method. The experimental results show that the method can accurately calculate the crowd panic level, which is important for guiding crowd evacuation.

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