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IEEE Transactions on Visualization and Computer Graphics

Senem Ezgi Emgin, Amirreza Aghakhani, Metin Sezgin, Cagatay Basdogan
We present HapTable; a multi-modal interactive tabletop that allows users to interact with digital images and objects through natural touch gestures, and receive visual and haptic feedback accordingly. In our system, hand pose is registered by an infrared camera and hand gestures are classified using a Support Vector Machine (SVM) classifier. To display a rich set of haptic effects for both static and dynamic gestures, we integrated electromechanical and electrostatic actuation techniques effectively on tabletop surface of HapTable, which is a surface capacitive touch screen...
July 11, 2018: IEEE Transactions on Visualization and Computer Graphics
Ernst Kruijff, Jason Orlosky, Naohiro Kishishita, Christina Trepkowski, Kiyoshi Kiyokawa
In Augmented Reality (AR), search performance for outdoor tasks is an important metric for evaluating the success of a large number of AR applications. Users must be able to find content quickly, labels and indicators must not be invasive but still clearly noticeable, and the user interface should maximize search performance in a variety of conditions. To address these issues, we have set up a series of experiments to test the influence of virtual characteristics such as color, size, and leader lines on the performance of search tasks and noticeability in both real and simulated environments...
July 10, 2018: IEEE Transactions on Visualization and Computer Graphics
Jingwu He, Linbo Wang, Wenzhe Zhou, Hongjie Zhang, Xiufen Cui, Yanwen Guo
This paper studies the problem of how to assess the quality of photographing viewpoints and how to choose good viewpoints for taking photographs of architectures. We achieve this by learning from photographs of world famous landmarks that are available on the Internet and their viewpoint quality ranked by online user annotation. Unlike previous efforts devoted to photo quality assessment which mainly rely on 2D image features, we show in this paper combining 2D image features extracted from images with 3D geometric features computed on the 3D models can result in more reliable evaluation of viewpoint quality...
July 6, 2018: IEEE Transactions on Visualization and Computer Graphics
Junpeng Wang, Subhashis Hazarika, Cheng Li, Han-Wei Shen
Over the last decade, ensemble visualization has witnessed a significant development due to the wide availability of ensemble data, and the increasing visualization needs from a variety of disciplines. From the data analysis point of view, it can be observed that many ensemble visualization works focus on the same facet of ensemble data, use similar data aggregation or uncertainty modeling methods. However, the lack of reflections on those essential commonalities and a systematic overview of those works prevents visualization researchers from effectively identifying new or unsolved problems and planning for further developments...
July 6, 2018: IEEE Transactions on Visualization and Computer Graphics
Joose Julius Rajamaki, Perttu Hamalainen
Efficient algorithms for 3D character control in continuous control setting remain an open problem in spite of the remarkable recent advances in the field. We present a sampling-based model-predictive controller that comes in the form of a Monte Carlo tree search (MCTS). The tree search utilizes information from multiple sources including two machine learning models. This allows rapid development of complex skills such as 3D humanoid locomotion with less than a million simulation steps, in less than a minute of computing on a modest personal computer...
July 2, 2018: IEEE Transactions on Visualization and Computer Graphics
Jie Li, Siming Chen, Kang Zhang, Gennady Andrienko, Natalia Andrienko
Spatial time series is a common type of data dealt with in many domains, such as economic statistics and environmental science. There have been many studies focusing on finding and analyzing various kinds of events in time series; the term 'event' refers to significant changes or occurrences of particular patterns formed by consecutive attribute values. We focus on a further step in event analysis: finding and exploring events that frequently co-occurred with a target class of similar events having occurred repeatedly over a period of time...
June 29, 2018: IEEE Transactions on Visualization and Computer Graphics
Junior Rojas, Tiantian Liu, Ladislav Kavan
We propose Average Vector Field (AVF) integration for simulation of deformable solids in physics-based animation. Our method achieves exact energy conservation for the St. Venant-Kirchhoff material without any correction steps or extra parameters. Exact energy conservation implies that our resulting animations 1) cannot explode and 2) do not suffer from numerical damping, which are two common problems with previous numerical integration techniques. Our method produces lively motion even with large time steps as typically used in physics-based animation...
June 28, 2018: IEEE Transactions on Visualization and Computer Graphics
Allan Carlos Avelino Rocha, Julio Daniel Silva, Usman Raza Alim, Sheelagh Carpendale, Mario Costa Sousa
We present decal-lenses, a new interaction technique that extends the concept of magic lenses to augment and manage multivariate visualizations on arbitrary surfaces. Our object-space lenses follow the surface geometry and allow the user to change the point of view during data exploration while maintaining a spatial reference to positions where one or more lenses were placed. Each lens delimits specific regions of the surface where one or more attributes can be selected or combined. Similar to 2D lenses, the user interacts with our lenses in real-time, switching between different attributes within the lens context...
June 26, 2018: IEEE Transactions on Visualization and Computer Graphics
Stefan Bruckner, Tobias Isenberg, Timo Ropinski, Alexander Wiebel
We discuss the concept of directness in the context of spatial interaction with visualization. In particular, we propose a model that allows practitioners to analyze and describe the spatial directness of interaction techniques, ultimately to be able to better understand interaction issues that may affect usability. To reach these goals, we distinguish between different types of directness. Each type of directness depends on a particular mapping between different spaces, for which we consider the data space, the visualization space, the output space, the user space, the manipulation space, and the interaction space...
June 25, 2018: IEEE Transactions on Visualization and Computer Graphics
Hao Tian, Changbo Wang, Dinesh Manocha, Xinyu Zhang
We present a realtime virtual grasping algorithm to model interactions with virtual objects. Our approach is designed for multi-fingered hands and makes no assumptions about the motion of the user's hand or the virtual objects. Given a model of the virtual hand, we use machine learning and particle swarm optimization to automatically pre-compute stable grasp configurations for that object. The learning pre-computation step is accelerated using GPU parallelization. At runtime, we rely on the pre-computed stable grasp configurations, and dynamics/non-penetration constraints along with motion planning techniques to compute plausible looking grasps...
June 21, 2018: IEEE Transactions on Visualization and Computer Graphics
Zhenyu Shu, Shiqing Xin, Xin Xu, Ligang Liu, Ladislav Kavan
Considering the fact that points of interest on 3D shapes can be discriminated from a geometric perspective, it is reasonable to map the geometric signature of a point to a probability value encoding to what degree is a point of interest, especially for a specific class of 3D shapes. Based on the observation, we propose a three-phase algorithm for learning and predicting points of interest on 3D shapes by using multiple feature descriptors. Our algorithm requires two separate deep neural networks (stacked auto-encoders) to accomplish the task...
June 19, 2018: IEEE Transactions on Visualization and Computer Graphics
Katerina Vrotsou, Aida Nordman
Sequential pattern mining finds applications in numerous diverging fields. Due to the problem's combinatorial nature, two main challenges arise. First, existing algorithms output large numbers of patterns many of which are uninteresting from a user's perspective. Second, as datasets grow, mining large number of patterns gets computationally expensive. There is, thus, a need for mining approaches that make it possible to focus the pattern search towards directions of interest. This work tackles this problem by combining interactive visualization with sequential pattern mining in order to create a "transparent box" execution model...
June 18, 2018: IEEE Transactions on Visualization and Computer Graphics
Luis Gustavo Nonato, Michael Aupetit
Visual analysis of multidimensional data requires effective ways to reduce data dimensionality to encode them visually. Multidimensional projections (MDP) figure among the most important visualization techniques in this context, transforming multidimensional data into scatter plots where patterns reflect some notion of similarity in the data. However, MDP come with distortions that make visual patterns not trustworthy. Moreover, the patterns present in scatter plots might not be enough to allow an understanding of multidimensional data, motivating the development of layout enrichment methodologies that operate with MDP...
June 13, 2018: IEEE Transactions on Visualization and Computer Graphics
Huayuan Guo, Youjiang Guan, Minchao Liu, Kaihuai Qin
In recent years, some biorthogonal Catmull-Clark subdivision wavelets constructed via lifting scheme have been proposed to speed up processing of geometric models. Thanks to the idea of progressive interpolation, the compression qualities and noise-filtering effects have been improved significantly. However, the reconstruction precision fails to be improved further because many model details are removed and the noise-filtering performance decreases greatly while the noise intensity increases gradually. To deal with this dilemma, a unified Catmull-Clark subdivision based biorthogonal wavelet construction with shape control parameters is presented to process 3D models with sharp-feature constraints...
June 11, 2018: IEEE Transactions on Visualization and Computer Graphics
Fred Matthew Hohman, Minsuk Kahng, Robert Pienta, Duen Horng Chau
Deep learning has recently seen rapid development and received significant attention due to its state-of-the-art performance on previously-thought hard problems. However, because of the internal complexity and nonlinear structure of deep neural networks, the underlying decision making processes for why these models are achieving such performance are challenging and sometimes mystifying to interpret. As deep learning spreads across domains, it is of paramount importance that we equip users of deep learning with tools for understanding when a model works correctly, when it fails, and ultimately how to improve its performance...
June 4, 2018: IEEE Transactions on Visualization and Computer Graphics
Balazs Kovacs, Peter 'Donovan, Kavita Bala, Aaron Hertzmann
Graphic design tools provide powerful controls for expert-level design creation, but the options can often be overwhelming for novices. This paper proposes Context-Aware Asset Search tools that take the current state of the user's design into account, thereby providing search and selections that are compatible with the current design and better fit the user's needs. In particular, we focus on image search and color selection, two tasks that are central to design. We learn a model for compatibility of images and colors within a design, using crowdsourced data...
June 4, 2018: IEEE Transactions on Visualization and Computer Graphics
Johanna Schmidt, Dominik Fleischmann, Bernhard Preim, Norbert Brandle, Gabriel Mistelbauer
Visualizing temporal data is inherently difficult, due to the many aspects that need to be communicated to the users. This is an important topic in visualization, and a wide range of visualization techniques dealing with different tasks have already been designed. In this paper we propose popup-plots, a novel concept where the common interaction of 3D rotation is used to navigate through the data. This allows the users to view the data from different perspectives without having to learn and adapt to new interaction concepts...
May 29, 2018: IEEE Transactions on Visualization and Computer Graphics
Xiaojie Xu, Chang Liu, Youyi Zheng
In this paper, we present a novel approach for 3D dental model segmentation via deep Convolutional Neural Networks (CNNs). Traditional geometry-based methods tend to receive undesirable results due to the complex appearance of human teeth (e.g., missing/rotten tooth, feature-less regions, crowding teeth, extra medical attachments, etc.). To address these issues, we propose to learn a generic and robust segmentation model by exploiting deep neural networks, namely NNs. To this end, we extensively experiment with various network structures, and eventually arrive at a two-level hierarchical CNNs structure for tooth segmentation: one for teeth-gingiva labelling and the other for inter-teeth labelling...
May 22, 2018: IEEE Transactions on Visualization and Computer Graphics
Yiqun Wang, Dong-Ming Yan, Xiaohan Liu, Chengcheng Tang, Jianwei Guo, Xiaopeng Zhang, Peter Wonka
We introduce a novel algorithm for isotropic surface remeshing which progressively eliminates obtuse triangles and improves small angles. The main novelty of the proposed approach is a simple vertex insertion scheme that facilitates the removal of large angles, and a vertex removal operation that improves the distribution of small angles. In combination with other standard local mesh operators, e.g., connectivity optimization and local tangential smoothing, our algorithm is able to remesh efficiently a low-quality mesh surface...
May 18, 2018: IEEE Transactions on Visualization and Computer Graphics
Monique Meuschke, Steffen Oeltze-Jafra, Oliver Beuing, Bernhard Preim, Kai Lawonn
We present a Cerebral Aneurysm Vortex Classification (CAVOCLA) that allows to classify blood flow in cerebral aneurysms. Medical studies assume a strong relation between the progression and rupture of aneurysms and flow patterns. To understand how flow patterns impact the vessel morphology, they are manually classified according to predefined classes. However, manual classifications are time-consuming and exhibit a high inter-observer variability. In contrast, our approach is more objective and faster than manual methods...
May 15, 2018: IEEE Transactions on Visualization and Computer Graphics
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