Virtual Reality (VR) system is an effective experimental study tool within the areas of cognitive psychology, neurosciences and psychophysiology. In this study, a new VR system, called panorama manifestation (PM) system, was developed and it was compared with a regular desktop (DT) system for two experimental way finding tasks in terms of success rate (SR), completion time (CT), difficulty index (DI), recalling accuracy (RA), and confidence index (CI). Results showed that although participants had similar SRs and gave similar DIs with these two systems, participants in PM system had less CT, more accurate route recalling and more confidence in judgment. The results indicate that the proposed PM system had higher fidelity for some spatial task experiments and could provide more support for participants' way finding tasks.
{"title":"Development of Panorama Manifestation System and Its Comparison with Desk-Top System","authors":"F. Meng, W. Zhang, R. Yang","doi":"10.1109/ICVRV.2011.21","DOIUrl":"https://doi.org/10.1109/ICVRV.2011.21","url":null,"abstract":"Virtual Reality (VR) system is an effective experimental study tool within the areas of cognitive psychology, neurosciences and psychophysiology. In this study, a new VR system, called panorama manifestation (PM) system, was developed and it was compared with a regular desktop (DT) system for two experimental way finding tasks in terms of success rate (SR), completion time (CT), difficulty index (DI), recalling accuracy (RA), and confidence index (CI). Results showed that although participants had similar SRs and gave similar DIs with these two systems, participants in PM system had less CT, more accurate route recalling and more confidence in judgment. The results indicate that the proposed PM system had higher fidelity for some spatial task experiments and could provide more support for participants' way finding tasks.","PeriodicalId":239933,"journal":{"name":"2011 International Conference on Virtual Reality and Visualization","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129860318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automatically assigning keywords to 3D models is of great interest as it allows one to retrieve, index, organize and understand large collections of 3D models. Most Methods require high sample size for training, so the data quality is in high demand. For small scale labeled data set, we propose a semi-supervised method to realize the 3D models multiple semantic annotation, which needs only a small amount of hand tagged information provided by users. The proposed technique utilizes low-level shape features and the keywords are assigned using a graphed-based label transfer mechanism to expand the training dataset. A weighted metric learning method is used to learn the distance measure from the extended dataset. Then multiple semantic annotation task can be completed on the learned distance measure. The proposed method outperforms the current state-of-the-art methods on the small scale labeled dataset and large unlabelled dataset. We believe that such measure will provide a strong platform to label 3D models when a small amount of labeled models were given.
{"title":"3D Model Multiple Semantic Automatic Annotation for Small Scale Labeled Data Set","authors":"Feng Tian, Xukun Shen, Liu Xian-mei, Xie Hong-tao","doi":"10.1109/ICVRV.2011.54","DOIUrl":"https://doi.org/10.1109/ICVRV.2011.54","url":null,"abstract":"Automatically assigning keywords to 3D models is of great interest as it allows one to retrieve, index, organize and understand large collections of 3D models. Most Methods require high sample size for training, so the data quality is in high demand. For small scale labeled data set, we propose a semi-supervised method to realize the 3D models multiple semantic annotation, which needs only a small amount of hand tagged information provided by users. The proposed technique utilizes low-level shape features and the keywords are assigned using a graphed-based label transfer mechanism to expand the training dataset. A weighted metric learning method is used to learn the distance measure from the extended dataset. Then multiple semantic annotation task can be completed on the learned distance measure. The proposed method outperforms the current state-of-the-art methods on the small scale labeled dataset and large unlabelled dataset. We believe that such measure will provide a strong platform to label 3D models when a small amount of labeled models were given.","PeriodicalId":239933,"journal":{"name":"2011 International Conference on Virtual Reality and Visualization","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121161365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The traditional volumetric visual hull generating methods were not applicable to real-time objects due to frame by frame calculations. A fast new algorithm based on inter frame coherence was represented for fast reconstructing an object's visual hull from its silhouettes in this paper. Utilizing frame to frame coherence of inter frame visual hulls and motion estimation, the numbers of voxels that need to be calculated were adjusted dynamically without having to calculate all the voxels. The solution improved the time efficiency by decreasing the computation cost. The results of the experiments show that this generation algorithm is superior to some classical visual hull methods, as far as its reconstruction function for time complexity is concerned.
{"title":"A Visual Hull Algorithm of 3D Reconstruction Based on Interframe Coherence","authors":"Chen Guojun, Shen Bao-ming","doi":"10.1109/ICVRV.2011.16","DOIUrl":"https://doi.org/10.1109/ICVRV.2011.16","url":null,"abstract":"The traditional volumetric visual hull generating methods were not applicable to real-time objects due to frame by frame calculations. A fast new algorithm based on inter frame coherence was represented for fast reconstructing an object's visual hull from its silhouettes in this paper. Utilizing frame to frame coherence of inter frame visual hulls and motion estimation, the numbers of voxels that need to be calculated were adjusted dynamically without having to calculate all the voxels. The solution improved the time efficiency by decreasing the computation cost. The results of the experiments show that this generation algorithm is superior to some classical visual hull methods, as far as its reconstruction function for time complexity is concerned.","PeriodicalId":239933,"journal":{"name":"2011 International Conference on Virtual Reality and Visualization","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133428379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The reuse of motion capture data has become an important way to generate realistic motions. Retrieval of similar motion segments from large motion datasets accordingly serves as a fundamental problem for data-based motion processing methods. The retrieval task is difficult due to the spatio-temporal variances existing in human motion. With the increasing amount of data, the retrieval task has become even more time consuming. In this paper, we present a motion retrieval approach that is capable of extracting similar motion subsequences from very large motion databases given a query motion input. Our method employs BIRCH-based(Balanced Iterative Reducing and Clustering using Hierarchies) clustering method to incrementally cluster poses so as to effectively deal with very large datasets. An elastic LCS(longest common subsequence) algorithm is then proposed to discover the similar motion subsequences based on the posture clustering result. Finally, the motion patterns are extracted and stored, with each pattern containing a set of similar motions. In the runtime retrieval stage, as each stored pattern effectively compared with the query motion, the group of the similar motions is acquired. Experimental results show that our method successfully retrieves similar motions and outperforms the existing methods in time and space costs when applying to very large motion datasets.
运动捕捉数据的重用已成为生成逼真运动的重要途径。相应地，从大型运动数据集中检索相似的运动片段是基于数据的运动处理方法的一个基本问题。由于人体运动存在时空差异，检索任务比较困难。随着数据量的增加，检索任务变得更加耗时。在本文中，我们提出了一种运动检索方法，该方法能够在给定查询运动输入的情况下从非常大的运动数据库中提取相似的运动子序列。该方法采用基于birch (Balanced Iterative reduction and Clustering using Hierarchies)的聚类方法对姿态进行增量聚类，从而有效地处理非常大的数据集。然后提出了一种弹性LCS(最长公共子序列)算法，基于姿态聚类结果发现相似的运动子序列。最后，提取并存储运动模式，每个模式包含一组相似的运动。在运行时检索阶段，将每个存储模式与查询运动进行有效比较，获得相似运动组。实验结果表明，当应用于非常大的运动数据集时，我们的方法成功地检索了相似的运动，并且在时间和空间成本上优于现有的方法。
{"title":"Motion Data Retrieval from Very Large Motion Databases","authors":"Cheng Ren, Xiaoyong Lei, Guofeng Zhang","doi":"10.1109/ICVRV.2011.50","DOIUrl":"https://doi.org/10.1109/ICVRV.2011.50","url":null,"abstract":"The reuse of motion capture data has become an important way to generate realistic motions. Retrieval of similar motion segments from large motion datasets accordingly serves as a fundamental problem for data-based motion processing methods. The retrieval task is difficult due to the spatio-temporal variances existing in human motion. With the increasing amount of data, the retrieval task has become even more time consuming. In this paper, we present a motion retrieval approach that is capable of extracting similar motion subsequences from very large motion databases given a query motion input. Our method employs BIRCH-based(Balanced Iterative Reducing and Clustering using Hierarchies) clustering method to incrementally cluster poses so as to effectively deal with very large datasets. An elastic LCS(longest common subsequence) algorithm is then proposed to discover the similar motion subsequences based on the posture clustering result. Finally, the motion patterns are extracted and stored, with each pattern containing a set of similar motions. In the runtime retrieval stage, as each stored pattern effectively compared with the query motion, the group of the similar motions is acquired. Experimental results show that our method successfully retrieves similar motions and outperforms the existing methods in time and space costs when applying to very large motion datasets.","PeriodicalId":239933,"journal":{"name":"2011 International Conference on Virtual Reality and Visualization","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123394328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present methods to generate a stable and realistic force rendering between virtual hand and object for a hand rehabilitation system. There are multi-contact regions between hand and object. For each contact region, the virtual contact force is modeled upon the spring-mass model. As direct rendering with spring-mass model in a large stiffness will induce instability of the haptic device, we apply a force-smoothing method by limiting the maximum force variation to guarantee the stability in the haptic rendering. The system experiment results demonstrate that the proposed method can provide satisfactory stable haptic display. And the maximum virtual stiffness that the system can simulate increases from 0.7 N/mm to 1.6N/mm by using force smoothing process.
{"title":"Haptic Rendering of Virtual Hand with Force Smoothing","authors":"Miao Feng, Jiting Li, Ruoyin Zheng","doi":"10.1109/ICVRV.2011.11","DOIUrl":"https://doi.org/10.1109/ICVRV.2011.11","url":null,"abstract":"In this paper, we present methods to generate a stable and realistic force rendering between virtual hand and object for a hand rehabilitation system. There are multi-contact regions between hand and object. For each contact region, the virtual contact force is modeled upon the spring-mass model. As direct rendering with spring-mass model in a large stiffness will induce instability of the haptic device, we apply a force-smoothing method by limiting the maximum force variation to guarantee the stability in the haptic rendering. The system experiment results demonstrate that the proposed method can provide satisfactory stable haptic display. And the maximum virtual stiffness that the system can simulate increases from 0.7 N/mm to 1.6N/mm by using force smoothing process.","PeriodicalId":239933,"journal":{"name":"2011 International Conference on Virtual Reality and Visualization","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132929617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The contradiction between data quantity and rendering quality is a rather troublesome issue in Image-Based Rendering (IBR). In this paper, we present an adaptive sampling method to relieve the contradiction. This method determines the sampling positions by using minimum expectation error criterion of the signal waveform. It is a non-iterative sampling process and could reach a quite good rendering quality with a little date quantity. Also, the method uses each ray in a camera as an independent sample and adjusts them individually. We apply this method to the IBR sampling and reconstruction with a light field setup. The experimental results show that the rendering quality is 3.7~7.8dB higher than that of the traditional method with the same sample size. The PSNR is above 30dB with less than 15% of the original samples. This method can apply to 1-D, 2-D signals and other kinds of IBR technologies.
{"title":"Noniterative Adaptive Sampling for Image-Based Rendering","authors":"Wen Qin, Zhijiang Zhang","doi":"10.1109/ICVRV.2011.49","DOIUrl":"https://doi.org/10.1109/ICVRV.2011.49","url":null,"abstract":"The contradiction between data quantity and rendering quality is a rather troublesome issue in Image-Based Rendering (IBR). In this paper, we present an adaptive sampling method to relieve the contradiction. This method determines the sampling positions by using minimum expectation error criterion of the signal waveform. It is a non-iterative sampling process and could reach a quite good rendering quality with a little date quantity. Also, the method uses each ray in a camera as an independent sample and adjusts them individually. We apply this method to the IBR sampling and reconstruction with a light field setup. The experimental results show that the rendering quality is 3.7~7.8dB higher than that of the traditional method with the same sample size. The PSNR is above 30dB with less than 15% of the original samples. This method can apply to 1-D, 2-D signals and other kinds of IBR technologies.","PeriodicalId":239933,"journal":{"name":"2011 International Conference on Virtual Reality and Visualization","volume":"514 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116333269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Virtual experiments have been used in education for a long time. However, most of the virtual experiments are modeled only for some special subjects, and are programmed case by case. To handle these problems, an online system of Virtual Educational Experiments (VEEs) is designed to develop various virtual experiments and simplify the virtual experiment modeling by reusing the existed experiment components. Based on the system of VEEs, we proposed a method of modeling and visualizing dynamic experiment components. Firstly, the dynamic experiment components are modeled based on the combination of images and graphics. Then the visualization of the dynamic experiment components is designed for multidisciplinary virtual experiments. The system has been running in some colleges and universities for several months. The tests show that it can rapidly model dynamic experiment components and realistically visualize the dynamic experiment components for the system of VEEs.
{"title":"Modeling and Visualizing Dynamic Component for Virtual Educational Experiments","authors":"Ziqiang Yang, Xiaowu Chen, Guodong Jia","doi":"10.1109/ICVRV.2011.60","DOIUrl":"https://doi.org/10.1109/ICVRV.2011.60","url":null,"abstract":"Virtual experiments have been used in education for a long time. However, most of the virtual experiments are modeled only for some special subjects, and are programmed case by case. To handle these problems, an online system of Virtual Educational Experiments (VEEs) is designed to develop various virtual experiments and simplify the virtual experiment modeling by reusing the existed experiment components. Based on the system of VEEs, we proposed a method of modeling and visualizing dynamic experiment components. Firstly, the dynamic experiment components are modeled based on the combination of images and graphics. Then the visualization of the dynamic experiment components is designed for multidisciplinary virtual experiments. The system has been running in some colleges and universities for several months. The tests show that it can rapidly model dynamic experiment components and realistically visualize the dynamic experiment components for the system of VEEs.","PeriodicalId":239933,"journal":{"name":"2011 International Conference on Virtual Reality and Visualization","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127949916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To meet the demand for efficient automatic navigation in virtual complex indoor scenes, this paper presents an automatic navigation algorithm. The algorithm uses Dijkstra algorithm for path planning on complex indoor scenes graph, and uses the adaptive vector length algorithm for Bezier curve control point to smooth the path. The paper indicates that path planning in indoor scene is a weighted and non oriented graph's shortest path problem. The paper gives the shortest path searching step by Dijkstra algorithm. In order to eliminate the vision dither phenomenon and enhance the immersed feeling during navigation, the paper uses Bezier curve to smooth path. According to the path included angle and distance between nodes, the paper proposes an adaptive vector length algorithm for curve control point. Finally, an example about marine engine room virtual scene navigation system was given. The algorithm given in this paper was demonstrated to be significant in complex indoor scene navigation.
{"title":"Automatic Navigation Algorithm in Virtual Complex Indoor Scenes","authors":"Zeng Hong, Zhang Jundong, Jiang Ruizheng, Feng Jinhong","doi":"10.1109/ICVRV.2011.29","DOIUrl":"https://doi.org/10.1109/ICVRV.2011.29","url":null,"abstract":"To meet the demand for efficient automatic navigation in virtual complex indoor scenes, this paper presents an automatic navigation algorithm. The algorithm uses Dijkstra algorithm for path planning on complex indoor scenes graph, and uses the adaptive vector length algorithm for Bezier curve control point to smooth the path. The paper indicates that path planning in indoor scene is a weighted and non oriented graph's shortest path problem. The paper gives the shortest path searching step by Dijkstra algorithm. In order to eliminate the vision dither phenomenon and enhance the immersed feeling during navigation, the paper uses Bezier curve to smooth path. According to the path included angle and distance between nodes, the paper proposes an adaptive vector length algorithm for curve control point. Finally, an example about marine engine room virtual scene navigation system was given. The algorithm given in this paper was demonstrated to be significant in complex indoor scene navigation.","PeriodicalId":239933,"journal":{"name":"2011 International Conference on Virtual Reality and Visualization","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124416106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a fast automatic and robust image registration algorithm is presented. First, we use the Fourier transform to calculate the relative position between the input images and sort the unordered image sequence. For two images with overlap region, we detect the feature points in the part of the region in one of the image with the robust method of SIFT. According to the coordinates of the feature points and the position relationship of two images, we can compute the space of the feature points in another image and detect the feature points in this region. Then, we use the geometric relation to reduce some mismatch points with structurally similar and achieve the robust image registration. We demonstrate illustrative results obtained by comparing and contrasting our output with other methods. This method can detect the effective feature points quickly for reducing the detection range of the feature points without human intervention and reduce the running time greatly in ensuring the feature points matching accuracy.
{"title":"A Fast Automatic and Robust Image Registration Algorithm","authors":"Qi Zhang, Zhijiang Zhang, Dan Zeng","doi":"10.1109/ICVRV.2011.53","DOIUrl":"https://doi.org/10.1109/ICVRV.2011.53","url":null,"abstract":"In this paper, a fast automatic and robust image registration algorithm is presented. First, we use the Fourier transform to calculate the relative position between the input images and sort the unordered image sequence. For two images with overlap region, we detect the feature points in the part of the region in one of the image with the robust method of SIFT. According to the coordinates of the feature points and the position relationship of two images, we can compute the space of the feature points in another image and detect the feature points in this region. Then, we use the geometric relation to reduce some mismatch points with structurally similar and achieve the robust image registration. We demonstrate illustrative results obtained by comparing and contrasting our output with other methods. This method can detect the effective feature points quickly for reducing the detection range of the feature points without human intervention and reduce the running time greatly in ensuring the feature points matching accuracy.","PeriodicalId":239933,"journal":{"name":"2011 International Conference on Virtual Reality and Visualization","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122421983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuetong Luo, Jinsheng Chen, Hanbin Wang, Deqing Qu, Jie Wang, Wenmin Tan
The paper focuses on appearance and spatial property-based volume classification with a painting interface. Two key problems exist, i.e., effectively propagating user-given painting to the entire volume and intuitively controlling the roles of appearance and spatial properties, which are not well solved in existing painting-based transfer function specification methods. The present paper formulates painting propagation as a function interpolation problem in a high-dimensional affine space and solves it effectively using Gaussian radial basis functions. For the second problem, the present work presents a two-step approach, which first propagates the user-given painting to the entire volume using appearance- and spatial-property-dominated feature vectors, and then combines the painting propagation results using an ellipsoid Gaussian transfer function (ETF) for volume classification. The user can intuitively manipulate ETF using system-provided widgets. The effectiveness of the proposed method has been verified on several datasets.
{"title":"Flexible Painting-Based Volume Classification Using Ellipsoid Gaussian Transfer Function","authors":"Yuetong Luo, Jinsheng Chen, Hanbin Wang, Deqing Qu, Jie Wang, Wenmin Tan","doi":"10.1109/ICVRV.2011.28","DOIUrl":"https://doi.org/10.1109/ICVRV.2011.28","url":null,"abstract":"The paper focuses on appearance and spatial property-based volume classification with a painting interface. Two key problems exist, i.e., effectively propagating user-given painting to the entire volume and intuitively controlling the roles of appearance and spatial properties, which are not well solved in existing painting-based transfer function specification methods. The present paper formulates painting propagation as a function interpolation problem in a high-dimensional affine space and solves it effectively using Gaussian radial basis functions. For the second problem, the present work presents a two-step approach, which first propagates the user-given painting to the entire volume using appearance- and spatial-property-dominated feature vectors, and then combines the painting propagation results using an ellipsoid Gaussian transfer function (ETF) for volume classification. The user can intuitively manipulate ETF using system-provided widgets. The effectiveness of the proposed method has been verified on several datasets.","PeriodicalId":239933,"journal":{"name":"2011 International Conference on Virtual Reality and Visualization","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125389662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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