IEEE Computer Graphics and Applications

• PrePrint: Advanced Volume Illumination with Unconstrained Light Source Positioning
  The application of advanced illumination models is known to support depth perception. However, in the context of volume rendering gradient-based shading models are still the standard. This is due to the fact that the computation of more advanced lighting effects is expensive, and therefore has a strong impact on rendering performance. Nevertheless, interactive frame rates are important for both, the interactive design of visual representations as well as the exploration process. We present an interactive volumetric lighting model, which simulates scattering and shadowing to generate high quality images. Besides the superior image quality as compared to gradient-based shading, in many cases we also achieve higher frame rates. To evaluate our model we have conducted a user study in which the participants had to perform depth perception tasks. The results of this study indicate that depth perception is significantly improved, when comparing our illumination model to conventional gradient-based volume shading. Additionally, since our volumetric illumination model is not based on a gradient computation, it is also less sensitive to noise and therefore also applicable to imaging modalities suffering from a low signal-to-noise ratio, such as magnetic resonance imaging or 3D ultrasound.
  
  
  Light - Rendering - Graphics - Health - 3D
  
  
• PrePrint: Context-Aware Motion Diversification for Crowd Simulation
  Traditional crowd simulation models typically focus on the navigational path-finding and local collision avoidance aspects. Relatively few existing efforts explore how to optimally control individual agents' detailed motions throughout a crowd. This paper proposes a novel scheme for dynamically controlling motion styles of agents to increase the motion variety of a crowd. The central idea of our scheme is to maximize the style variety of local neighbors and global style utilization while maintaining the style consistency for each agent as natural as possible. Our scheme can serve as a complementary layer for most high-level crowd models to increase the variety realism. We show the flexibility and superiority of our scheme over traditional random motion style distribution through several experiment scenarios and user evaluations. To assist the runtime diversity control, an off-line preprocessing algorithm is also proposed to extract and stylize primitive motions from a motion capture database.
  
  
  
  
• PrePrint: Real Time Camera Pose Estimation for Wide Area Augmented Reality Applications
  Registration between real and synthetic worlds is one of the most difficult problems in augmented reality systems. To solve the registration problem for wide area AR applications, we propose a real time camera pose estimation method. In this paper, two main contributions are: Firstly, a method based on multiple maps and local bundle adjustment is proposed to solve the online scene reconstructing problem. The method enables the registration to work without the prior knowledge of natural scenes, which really enhance the usability of AR systems. Secondly, we redefine the class of the traditional ferns and propose a new recognition method to perform the online keyframes learning and recognition to obtain a system that has the ability to learn keyframes dynamically and switch between different scenes automatically even in large scale workspaces. Some results are introduced to validate the performance of the proposed method.
  
  
  
  
• PrePrint: From Styling Design to Products Fabricated by Planar Materials
  This article describes a geometric modeling system that generates industry required planar pieces for fabricating user-customized products from styling designs. The processing from style design to industrial patterns is automated. Pre-stored styling designs can be automatically mapped into different reference model shapes and then unfolded into planar pieces. Besides, a map-guided algorithm has been developed to locate unfolded pieces according to industrial requirement.
  
  
  
  
• PrePrint: Prajna: Adding Automated Reasoning to the Visual Analysis Process
  Applications and systems can represent knowledge in a variety of ways. A graphic display might allow a knowledge analyst to infer new information through interactive visualizations. Knowledge can be represented as a collection of facts, which can then be used for automatic inference. Knowledge can also be represented or stored in various archives, such as databases or formatted files. Those developers challenged with creating applications for knowledge representation frequently have to contend not only with data challenges, but also with challenges caused by a wide variety of software toolkits, architectures, and standards for knowledge representation. To meet these obstacles, we developed the Prajna Project. The Prajna Project is a Java toolkit designed to provide various capabilities for visualization, knowledge representation, geographic displays, semantic reasoning, and data fusion. Within this paper, we present both the capabilities of the Prajna project, and use it to illustrate techniques that address these challenges.
  
  
  
  
• PrePrint: Intuitive Interactive Human Character Posing with Millions of Example Poses
  We present a data-driven algorithm for interactive 3D human character posing. We formulate the problem in a maximum a posteriori (MAP) framework by combining the user's inputs with the priors embedded in prerecorded human poses. Maximizing the posteriori allows us to generate a most likely human pose that satisfies the user constraints. One unique property of our system is its ability to learn priors from a huge and heterogeneous human motion capture database (2.8 million prerecorded poses) and use them to generate a wide range of natural poses, a capacity that has not been demonstrated in previous data-driven character posing systems. In addition, we present two intuitive interfaces for interactive human character posing: direct manipulation interfaces and sketching interfaces. We show the superiority of our system by comparing it with standard inverse kinematics techniques as well as to alternative data-driven techniques.
  
  
  
  
• PrePrint: The Impact of the OCEAN Personality Model on the Perception of Crowds
  Most current crowd simulators animate homogeneous crowds, but include underlying parameters that can be tuned to create variations within the crowd. These parameters, however, are specific to the crowd models and may be difficult for an animator or naïve user to use. We propose mapping these parameters to personality traits. In this paper, we extend the HiDAC (High-Density Autonomous Crowds) system by providing each agent with a personality model. We use the OCEAN personality model as a basis for agent psychology. To each personality trait we associate nominal behaviors; thus, specifying personality for an agent leads to an automation of the low-level parameter tuning process. We describe a plausible mapping from personality traits to the existing behavior types and analyze the overall emergent crowd behaviors. Finally, we validate our mapping by user studies that assess the perception of the traits in the animations illustrating such behaviors.
  
  
  
  
• PrePrint: Context-preserving Dynamic Word Cloud Visualization
  In this paper, we propose context-preserving dynamic word clouds, extended from traditional word clouds, to illustrate the content evolution in a large collection of dynamic text documents. Our method can generate a sequence of word clouds in which the related words will be grouped together in individual word clouds and the positions of words will be preserved in the whole sequence. In addition, we introduce a trend chart to summarize the content changes in the generated word cloud sequence. Combining these two techniques, we develope a visualization system helping users explore and derive insight from a large collection of documents. The experiments with several real datasets have demonstrated the effectiveness of our system.
  
  
  Tag cloud - Dictionaries - Word Processors - Vocabulary Lists - Word
  
  
• IEEE Computer Graphics and Applications - September/October 2010 (Vol. 30, No. 5)
  IEEE Computer Graphics and Applications
  
  
  
  
• PrePrint: Visualizing Graphs and Clusters as Maps
  Information visualization is essential in making sense out of large data sets. Often, high-dimensional data are visualized as a collection of points in 2-dimensional space through dimensionality reduction techniques. However, these traditional methods often do not capture well the underlying structural information, clustering, and neighborhoods. In this paper, we describe GMap, a practical algorithmic framework for visualizing relational data with geographic-like maps. We illustrate the effectiveness of this approach with examples from several domains.
  
  
  Data - Knowledge Management - Knowledge Discovery - Information Visualization - Visualization