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2013中国虚拟现实大会暨ICVRV'2013

作者： Xin Gao Yong Hu Qing Zuo Yue Qi State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Technology Beihang University BeijingChina

This paper develops a laser sheet scanning based technique for capturing and reconstructing sequential volumetric models of ***,a dedicated setup is introduced as the laser sheet illuminator in horizontal *** achieve an accurate acquisition,a signal synchronized scheme is added between the galvanometer and the high-speed ***,with a laser sheet sweeping through the volume repeatedly,the illuminated smoke slices are *** sweep of the laser records a near-simultaneous smoke density *** next reconstruction procedure,through camera and laser calibrations,3D real positions of the pixels of captured images is ***,these irregular smoke density fields are resampled by a 3D original Kriging interpolation algorithm and are reconstructed to regular smoke volumetric *** experimental results,the fidelity of visualized smoke volumetric models reconstructed by our smoke modeling method demonstrates that our approach can make a good effect on realistic smoke modeling.

关键词： Laser sheet scanning Smoke acquisition Volume reconstruction Kiring interpolation

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A metadata extraction approach from papers based on meta-learning

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Journal of Information and Computational Science 2013年第4期10卷 1121-1129页

作者： Zhang, Fuzhi Zhao, Zihao School of Information Science and Engineering The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao 066004 China

To improve the accuracy of paper metadata extraction, a paper metadata extraction approach based on meta-learning is presented. Firstly, we propose a construction method of base-classifiers, which combines the Support Vector Machine (SVM) with the created diverse base-level training sets to construct some base-classifiers with larger diversity. The training sets are created according to the Open Access (OA) journal categories. Secondly, we present a paper metadata extraction algorithm based on meta-learning, which uses the meta-classifier to integrate the classification results of the base-classifiers and generates the final extraction results. The proposed approach guarantees extraction accuracy and independence of the base-classifiers, increases the diversities of them, and improves the classification performance of the meta-classifier, as well as reduces the degree of correlation of misclassifications. The experimental results show that our approach is superior to other single machine learning algorithm and the accuracy of paper metadata extraction is improved. © 2013 by Binary Information Press.

关键词： Support vector machines

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HintVis:The Hierarchical Visualization of Network Traffic Data

HintVis:The Hierarchical Visualization of Network Traffic Da...

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2013中国虚拟现实大会暨ICVRV'2013

作者： Hengyuan Zhang Xiaowu Chen Haifeng Hu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University BeijingChina

The network traffic data is interpreted differently by people in different *** paper proposes a framework for hierarchically visualizing the network traffic data,and customizes a set of the classic approaches or algorithms to produce visualizations in different *** on the framework,we developed a prototype system HintVis to support analyzing network traffic data in different levels by constructing layered semantic network traffic objects and producing hierarchical *** usability of HintVis is demonstrated by visualizing packets going through the gateway in a *** on the hierarchical visualizations,analysts are able to semantically navigate in the network traffic data and concentrate on what they need.

关键词： Network Traffic Hierarchical Visualization User Interface

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Geometric Based Structure Propagation and Texture Matching for 3D Texture Completion

Geometric Based Structure Propagation and Texture Matching f...

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2013中国虚拟现实大会暨ICVRV'2013

作者： Kan Guo Xiaowu Chen Yili Liu Bin Zhou Yu Guo State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang UniversityBeijingChina

This paper proposes a novel method to complete the missing appearance of mesh surfaces through structure propagation and texture *** advance,the appearance on surfaces is divided into structure and texture ***,the missing structures are predicted through structure *** structure propagation is guided by surfaces geometric features by using 3D tensor voting and crest line ***,with the guidance of predicted structures,the optimal local sample patches are matched to inpaint the missing ***,the completed result is obtained by mapping the optimal patches to the missing texture regions on *** acquire convincing completion results as shown in the experiments and comparisons.

关键词： structure propagation texture completion tensor voting

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Visualizing Network Communication in Geographic Environment

Visualizing Network Communication in Geographic Environment

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2013中国虚拟现实大会暨ICVRV'2013

作者： Haifeng Hu Hengyuan Zhang Wei Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engine Computer and Information Science Southwest University Chongqing China

Geographic network visualization lay out the data with respect to the physical location of nodes in the *** paper presents a method for visualizing network communication in geographic *** method first simplifies GIS data and aggregates network communication data according to both geographic distribution and network topology,and then followed by selecting and highlighting main communication ***,the layout of some nodes is adjusted in the *** using our method,the network communication data can be visualized in geographic environment aesthetically and *** developed a prototype system to help analysts and users understanding the characteristics and patterns of the networks communication.

关键词： Network Communication Visualization Geographic

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The synthesis of dynamic junction over-constrained parallel-parallel mechanism based on the weighted directed graph theory

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ICIC Express Letters 2013年第11期7卷 3057-3065页

作者： Guo, Xijuan Zhang, Dayu Geng, Qingjia Zhang, Wenyuan The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University No. 438 Hebei Ave. Qinhuangdao 066004 China

Several dynamic junction over-constrained parallel-parallel mechanisms are proposed in this paper with a new method based on graph theory synthesis. This kind of mechanism is the pivotal part of the equivalent mechanism in helicopter automatic tilting device. The purpose of synthesis is that the synthesis result is helpful to optimize the structure and optimal design for helicopter automatic tilting device, and the reason why a graph method is chosen is that it can be programmed easily. The characteristic of "Dynamic Junction" is proposed relative to fixed junction. Because the joint of connecting two parallel mechanisms is dynamic, these two mechanisms can motion relatively. A mount of permutation and combination calculating processes are needed because of dynamic characteristic. In this view, choosing a synthesis method which can program easily in computer is appropriate. The graph method is the best choice. However, classic graph method is difficult to discriminate the graph isomorphism and limited to apply in spatial mechanism. Therefore, this paper proposed a new graph way. First, mechanism is modeled in weighted directed graph, then based on equal freedom of degree, many other graph models are got which have the same motion characteristic. Finally many target mechanisms can be derived by those models. The new method avoids the problem of classic graph method and is easy to program and run in computer, it is proved effectively because several dynamic junction over-constrained parallel-parallel mechanisms are successfully synthesized by it. © 2013 ISSN 1881-803X.

关键词： Dynamics

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UNSUPERVISED IMAGE SEGMENTATION USING GLOBAL SPATIAL CONSTRAINT AND MULTI-SCALE REPRESENTATION ON MULTIPLE SEGMENTATION PROPOSALS

UNSUPERVISED IMAGE SEGMENTATION USING GLOBAL SPATIAL CONSTRA...

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IEEE International Conference on Image Processing

作者： Linjia Sun Xiaohui Liang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing 100191 China

ISBN: (纸本)9781479923427

This paper presents a novel method for unsupervised image segmentation. The method determines the reasonable segments for final segmentation by exploiting both global and local context cues on multiple segmentation proposals. The proposal is obtained by using any existing segmentation algorithms, providing the diverse segment cues to guide segmentation. An iterative process is used to perform the cues integration and the image segmentation, including the segments modeling and the segments labeling. The former estimates the distribution of shared segments, while the latter labels each proposal into segments by minimizing an energy function. The final segmentation is produced when the consistent spatial layout is found in different proposals. Compared with the existing methods, the segmentation results are more satisfying on the Berkeley Segmentation Database.

关键词： Unsupervised segmentation spatial layout segmentation proposals multi-scale representation space layout image segmentation segmentation Segmentation multi-scale Segments images

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Image Matting with Local and Nonlocal Smooth Priors

Image Matting with Local and Nonlocal Smooth Priors

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IEEE Conference on computer Vision and Pattern Recognition

作者： Xiaowu Chen Dongqing Zou Steven ZhiYing Zhou Qinping Zhao Ping Tan State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Department of Electrical & Computer Engineering National University of Singapore

ISBN: (纸本)9781467364102

In this paper we propose a novel alpha matting method with local and nonlocal smooth priors. We observe that the manifold preserving editing propagation [4] essentially introduced a nonlocal smooth prior on the alpha matte. This nonlocal smooth prior and the well known local smooth prior from matting Laplacian complement each other. So we combine them with a simple data term from color sampling in a graph model for nature image matting. Our method has a closed-form solution and can be solved efficiently. Compared with the state-of-the-art methods, our method produces more accurate results according to the evaluation on standard benchmark datasets.

关键词： Graphical models standardized benchmark Imagery (Psychotherapy) Image images Adrenergic alpha-Antagonists editing Laplacian operator

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MULTI-SCALE, MULTI-LEVEL, HETEROGENEOUS FEATURES EXTRACTION AND CLASSIFICATION OF VOLUMETRIC MEDICAL IMAGES

MULTI-SCALE, MULTI-LEVEL, HETEROGENEOUS FEATURES EXTRACTION ...

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IEEE International Conference on Image Processing

作者： Shuai Li Qinping Zhao Shengfa Wang Aimin Hao Hong Qin State Key Laboratory of Virtual Reality Technology and Systems Beihang University China Department of Computer Science Stony Brook University USA

ISBN: (纸本)9781479923427

This paper articulates a novel method for the heterogeneous feature extraction and classification directly on volumetric images, which covers multi-scale point feature, multi-scale surface feature, multi-level curve feature, and blob feature. To tackle the challenge of complex volumetric inner structure and diverse feature forms, our technical solution hinges upon the integrated approach of locally-defined diffusion tensor (DT), DT-based anisotropic convolution kernel (DACK), DACK-based multi-scale analysis, and DT-governed curve feature growing. The extracted structural features can be further semantically classified. At the computational fronts, we design CUDA-based algorithm to conduct parallel computation for time consuming tasks. Various experiments and timing tests demonstrate the effectiveness, robustness, and high performance of our method.

关键词： Volumetric image Multi-scale heterogeneous features Curve propagation Diffusion tensor CUDA Feature extraction images multi-scale multilayers PARALLEL PROCESSING (computerS) HIGH-PERFORMANCE Taxonomy

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Activities Information Diffusion in Chinese Largest Recommendation Social Network: Patterns and Generative Model

Activities Information Diffusion in Chinese Largest Recommen...

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IEEE Global Communications Conference

作者： Jianwei Niu Shaluo Huang Lei Shu Ivan Stojmenovic State Key Laboratory of Virtual Reality Technology and Systems Beihang University Guangdong Petrochemical Equipment Fault Diagnosis Key Laboratory Guangdong University of Petrochemical Technology Electrical Engineering and Computer Science University of Ottawa

ISBN: (纸本)9781479913510

Nowadays, networks play an indispensable role in social life, and social networks have become a new advertising medium for offline activities. Previous studies of information diffusion or behavior spread over social networks have mostly focused on diffusion models and analysis of virtual interaction between online users, and very few of them focus on the propagation of real world activities in these social networks. To address this problem, we use data obtained from the Chinese largest recommendation social network - Douban, and study how the offline activities spread from one user to another through Douban. By using cascading subgraphs and diffusion trees, we break a whole cascade into local subgraphs. After analyzing the activities of about 1.47 million users, we observe the statistical and topological characteristics of these local cascading subgraphs. Next, we find the size and degree distributions of these cascading subgraphs and several common patterns of topology of local cascades. Moreover, we also have some other interesting discoveries, like the relation between the number of initial adopters and the final cascade size, and the underlying influences driving user behaviors. Finally, we propose a diffusion model that can generate information cascades that follow the patterns we have observed, and validate it by empirical analysis.

关键词： Social network Offline activity Information diffusion Underlying influence Generative models Social Networks cascades Social Media DIFFUSION MODELS Social Capital activity diffusion

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