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2018 IEEE International Conference on Information and Automation, ICIA 2018

作者： Li, Kai Cheng, Jun Zhang, Qieshi Liu, Jianming School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Chinese Academy of Sciences Shenzhen China Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781538680698

In this paper, we propose a human-computer interaction (HCI) system for entertainment or education via a depth-sensing camera. The whole system is comprised of three modules: hand detection, hand tracking, and gesture recognition. In our system, specifically, hand detection is based entirely on computer vision and do not use any markers. We utilize the Kalman filter and depth data from the Kinect to predict the hand position making the tracking smooth and robust. And we extract the apparent gestural features to recognize gestures, which are adaptable and quick. Two applications are in place to demonstrate the system: writing board, in which the user can freely draw lines in the air using their hand, and a classic game, Gluttonous snake, which is transformed into a novel game by using our framework. The system we develop is highly extendible and can be used in a wide variety of other interactive scenarios. © 2018 IEEE.

关键词： Gesture recognition

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Distortion-adaptive salient object detection in 360◦ omnidirectional images

arXiv

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arXiv 2019年

作者： Li, Jia Su, Jinming Xia, Changqun Tian, Yonghong State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Adavanced Innovation Center for Big Data and Brain Computing Beihang University China National Engineering Laboratory for Video Technology School of Electronics Engineering and Computer Science Peking University Beijing China Peng Cheng Laboratory Shenzhen518000 China

Image-based salient object detection (SOD) has been extensively explored in the past decades. However, SOD on 360◦ omnidirectional images is less studied owing to the lack of datasets with pixel-level annotations. Toward this end, this paper proposes a 360◦ image-based SOD dataset that contains 500 high-resolution equirectangular images. We collect the representative equirectangular images from five mainstream 360◦ video datasets and manually annotate all objects and regions over these images with precise masks with a free-viewpoint way. To the best of our knowledge, it is the first public available dataset for salient object detection on 360◦ scenes. By observing this dataset, we find that distortion from projection, large-scale complex scene and small salient objects are the most prominent characteristics. Inspired by these foundings, this paper proposes a baseline model for SOD on equirectangular images. In the proposed approach, we construct a distortion-adaptive module to deal with the distortion caused by the equirectangular projection. In addition, a multiscale contextual integration block is introduced to perceive and distinguish the rich scenes and objects in omnidirectional scenes. The whole network is organized in a progressively manner with deep supervision. Experimental results show the proposed baseline approach outperforms the top-performanced state-of-the-art methods on 360◦ SOD dataset. Moreover, benchmarking results of the proposed baseline approach and other methods on 360◦ SOD dataset show the proposed dataset is very challenging, which also validate the usefulness of the proposed dataset and approach to boost the development of SOD on 360◦ omnidirectional scenes. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

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Using a LSTM-RNN Based Deep Learning Framework for ICU Mortality Prediction 15th

Using a LSTM-RNN Based Deep Learning Framework for ICU Morta...

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15th Web Information Systems and Applications Conference, WISA 2018

作者： Zheng, Hanzhong Shi, Dejia Department of Computer Science University of Pittsburgh PittsburghPA15213 United States Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha410205 China

ISBN: (纸本)9783030029333

In Intensive Care Units (ICU), the machine learning technique has been widely used in ICU patient data. A mortality risky model can provide assessment on patients’ current and when the disease may worsen. The prediction of mortality outcomes even intervenes doctor’s decision making on patient’s treatment. Based on the patient’s condition, a timely intervention treatment is adopted to prevent the patient’s condition gets worse. However, the common major challenges in ICU patient data are irregular data sampling and missing variables values. In this paper, we used a statistical approach to preprocess the data. We introduced a data imputation method based on Gaussian process and proposed a deep learning technology using LSTM-RUN that emphasizes on long time dependency relation inside the patient data records to predict the probability of patient’s mortality in ICU. The experiment results show that LSTM improved the mortality prediction accuracy than base RNN using the new statistical imputation method for handling missing data problem. © 2018, Springer Nature Switzerland AG.

关键词： Forecasting

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Novel metaballs-driven approach with dynamic constraints for character articulation

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Science China(Information Sciences) 2018年第9期61卷 256-258页

作者： Junxuan BAI Junjun PAN Yuhan YANG Hong QIN State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beihang University Qingdao Research Institute Department of Computer Science Stony Brook University

Skinning techniques are essential for character articulation in 3D computer ***,skeleton-based methods are widely used in the animation industry for its simplicity and efficiency,especially in linear blend skinning(LB...

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Detecting shilling profiles in collaborative recommender systems via multidimensional profile temporal features

Detecting shilling profiles in collaborative recommender sys...

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作者： Hao, Yaojun Zhang, Fuzhi School of Information Science and Engineering Yanshan University Qinhuangdao Hebei Province China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao Hebei Province China Department of Computer Science and Technology Xinzhou Teachers University Xinzhou Shanxi Province China

To defend recommender systems, various methods have been proposed to detect shilling profiles, which can be categorised as user- and item-based detection methods. Most of the user-based methods identify shilling profiles via statistical signatures of rating values and suffer from low precision when detecting different types of attacks. Most of the item-based methods use temporal information to detect the anomaly items, but they assume that the fake ratings were injected in short periods. So they are invalid for the long duration and decentralised injection attacks. To address these limitations, the authors extract the multidimensional profile temporal features and present a shilling detection method. First, from the user profile view, user rating behaviours are characterised by corrected conditional entropy and the dissimilarity with the rest-rating model. Second, from the item profile view, the user features are extracted according to item temporal popularity. Third, the features based on weighted deviation from dynamic mean are extracted according to the fact that the items mean changes with time. Finally, support vector machine is exploited to detect shilling profiles based on the proposed features. Experimental results on the Netflix dataset indicate that the performance of the proposed method is better than that of the benchmark methods. © The Institution of Engineering and technology 2018.

关键词： User profile

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Synthesized prioritized data pruning based deep deterministic policy gradient algorithm improvement

Synthesized prioritized data pruning based deep deterministi...

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2018 IEEE International Conference on Information and Automation, ICIA 2018

作者： Xiang, Hui Cheng, Jun Zhang, Qieshi Liu, Jianming School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781538680698

In recent years, reinforcement learning has been widely applied in various fields and has achieved rapid development. Based on the deep deterministic policy gradient (DDPG) algorithm, we propose a synthesized prioritized data Pruning DDPG method. It proposes a solution to the problem of first-in-first-out (FIFO) storage methods and random sampling in the replay buffer: Selecting high-priority samples to the network for training, while removing similar samples in the buffer and retaining some rare samples. The model of the method is programmed with TensorFlow. The proposed method is verified by the Pendulum experiment which is performed on the OpenAI Gym platform. The experimental results indicate that our method can not only achieve similar performances in a shorter training time, but also accelerate the training process and improve the learning stability and long-term memory. © 2018 IEEE.

关键词： Reinforcement learning

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A CADe system for nodule detection in thoracic CT images based on artificial neural network

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Science China(Information Sciences) 2017年第7期60卷 177-194页

作者： Xinglong LIU Fei HOU Hong QIN Aimin HAO State Key Laboratory of Virtual Reality Technology and Systems Beihang University School of Computer Science and Engineering Nanyang Technological University Department of Computer Science Stony Brook University

Lung cancer has been the leading cause of cancer-related deaths in 2015 in United States. Early detection of lung nodules will undoubtedly increase the five-year survival rate for lung cancer according to prior studies. In this paper, we propose a novel rating method based on geometrical and statistical features to extract initial nodule candidates and an artificial neural network approach to the detection of lung nodules. The novel method is solely based on 3D distribution of neighboring voxels instead of user-specified features. During initial candidates detection, we combine organized region properties calculated from connected component analysis with corresponding voxel value distributions from statistical analysis to reduce false positives while retaining true nodules. Then we devise multiple artificial neural networks(ANNs) trained from massive voxel neighbor sampling of different types of nodules and organize the outputs using a 3D scoring method to identify final nodules. The experiments on 107 CT cases with 252 nodules in LIDC-IDRI data sets have shown that our new method achieves sensitivity of 89.4% while reducing the false positives to 2.0 per case. Our comprehensive experiments have demonstrated our system would be of great assistance for diagnosis of lung nodules in clinical treatments.

关键词： artificial neutral network Lung nodule computed tomography computer aided detection nodule detection

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Robust and efficient SPH simulation for high-speed fluids with the dynamic particle partitioning method 26

Robust and efficient SPH simulation for high-speed fluids wi...

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26th Pacific Conference on computer Graphics and Applications, Pacific Graphics 2018

作者： Zheng, Zhong Gao, Yang Li, Shuai Qin, Hong Hao, Aimin State Key Laboratory of Virtual Reality Technology and Systems Beihang University China Beihang University Qingdao Research Institute Beihang University China Department of Computer Science Stony Brook University United States

ISBN: (纸本)9783038680734

In this paper, our research efforts are devoted to the efficiency issue of the SPH simulation when the ratio of velocities among fluid particles is large. Specifically, we introduce a k-means clustering method into the SPH framework to dynamically partition fluid particles into two disjoint groups based on their velocities, we then use a two-scale time step scheme for these two types of particles. The smaller time steps are for particles with higher speed in order to preserve temporal details and guarantee the numerical stability. In contrast, the larger time steps are used for particles with smaller speeds to reduce the computational expense, and both types of particles are tightly coupled in the simulation. We conduct various experiments which have manifested the advantages of our methods over the conventional SPH technique and its new variants in terms of efficiency and stability. © 2018 The Author(s).

关键词： K-means clustering

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Recent advances on HEVC inter-frame coding: From optimization to implementation and beyond

arXiv

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arXiv 2019年

作者： Zhang, Yongfei Zhang, Chao Fan, Rui Ma, Siwei Chen, Zhibo Kuo, C.-C. Jay Beijing Key Laboratory of Digital Media School of Computer Science and Engineering State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China China Academy of Electronic and Information Technology Beijing100041 China Institute of Digital Media School of Electronics Engineering and Computer Science Peking University Beijing100871 China University of Science and Technology of China Hefei Anhui230026 China University of Southern California Los AngelesCA90089 United States

High Efficiency Video Coding (HEVC) has doubled the video compression ratio with equivalent subjective quality as compared to its predecessor H.264/AVC. The significant coding efficiency improvement is attributed to many new techniques. Inter-frame coding is one of the most powerful yet complicated techniques therein and has posed high computational burden thus main obstacle in HEVC-based real-time applications. Recently, plenty of research has been done to optimize the inter-frame coding, either to reduce the complexity for real-time applications, or to further enhance the encoding efficiency. In this paper, we provide a comprehensive review of the state-of-the-art techniques for HEVC inter-frame coding from three aspects, namely fast inter coding solutions, implementation on different hardware platforms as well as advanced inter coding techniques. More specifically, different algorithms in each aspect are further subdivided into sub-categories and compared in terms of pros, cons, coding efficiency and coding complexity. To the best of our knowledge, this is the first such comprehensive review of the recent advances of the inter-frame coding for HEVC and hopefully it would help the improvement, implementation and applications of HEVC as well as the ongoing development of the next generation video coding standard. Copyright © 2019, The Authors. All rights reserved.

关键词： Efficiency

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Dynamic community mining based on behavior prediction

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International Journal of Performability Engineering 2018年第7期14卷 1590-1599页

作者： Chen, Xiao Hu, Xinzhuan Pan, Xiao Guo, Jingfeng Network Technology Center Hebei Normal University of Science and Technology Qinhuangdao066004 China College of Economics and Management YanShan University Qinhuangdao066004 China College of Economic and Management Shijiazhuang Tiedao University Shijiazhuang050043 China College of Information Science and Engineering YanShan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao066004 China

Dynamic network research has been a new trend in recent years. Based on the influence of vertex behavior on community structure, this paper studies signed network dynamic community mining. Firstly, the set pair connection degree is introduced to describe the relation between vertices, and the edge prediction model of signed network is proposed by taking into account the variability of the relation between vertices. Secondly, based on the prediction model, a set pair signed networks dynamic model is proposed by adding time axis T to the signed network. Then, based on the dynamic model, the evolution of signed networks and community discovering are studied. Finally, network evolution law and community stability are analyzed by using the connection trend and connection entropy in set pair theory, and the accuracy and validity of the dynamic community mining algorithm are verified by experiments. © 2018 Totem Publishers Ltd. All rights reserved.

关键词： Dynamic models

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