Home Publications
Selected Publications (Selected papers/talks can be downloaded from My Favorite/By years/Talks webpages)
Selected List By years My Favorite Patents Talks    

2010    2009    2008    2007    2006    2005    2004    Before 2004

Real-Time Shape Illustration Using Laplacian Lines.
Long Zhang, Ying He, Jiazhi Xia, Xuexiang Xie, Wei Chen.

IEEE Transactions on Visualization and Computer Graphics , 2010.

This paper presents a novel object-space line drawing algorithm that can depict shapes with view-dependent feature lines in real time. Strongly inspired by the Laplacian-of-Gaussian (LoG) edge detector in image processing, we define Laplacian lines as the zero-crossing points of the Laplacian of the surface illumination. Compared to other view-dependent feature lines, Laplacian lines are computationally efficient because most expensive computations can be pre-processed. We further extend Laplacian lines to volumetric data and develop the algorithm to compute volumetric Laplacian lines without iso-surface extraction. We apply the proposed Laplacian lines to a wide range of real-world models and demonstrate that Laplacian lines are more efficient than the existing computer generated feature lines and can be used in interactive graphics applications.
pdf video bibtex slides

Digital Storytelling: Automatic Animation for Time-Varying Data Visualization
Li Yu, Aidong Lu, William Ribarsky, Wei Chen

To appear Computer Graphics Forum (Special Issue of Pacific Graphics 2010)

This paper presents a digital storytelling approach that generates automatic animations for time-varying data visualization. Our approach simulates the composition and transition of storytelling techniques and synthesizes animations to describe various event features. Specifically, we analyze information related to a given event and abstract it as an event graph, which represents data features as nodes and event relationships as links. This graph embeds a tree-like hierarchical structure which encodes data features at different scales. Next, narrative structures are built by exploring starting nodes and suitable search strategies in this graph. Different stages of narrative structures are considered in our automatic rendering parameter decision process to generate animations as digital stories. We integrate this animation generation approach into an interactive exploration process of timevarying data, so that more comprehensive information can be provided in a timely fashion. We demonstrate with a storm surge application that our approach allows semantic visualization of time-varying data and easy animation generation for users without special knowledge about the underlying visualization techniques.
pdf video bibtex slides

Motion Track: Visualizaing Motion Variation of Human Motion Data
Yueqi Hu, Shuangyuan Wu, Shihong Xia, Jinghua Fu, Wei Chen

In Proceedings of IEEE Pacific Visualization Symposium, March 2010, Taibei
(Cover Image)

This paper proposes a novel visualization approach, which can depict the variations between different human motion data. This is achieved by representing the time dimension of each animation sequence with a sequential curve in a locality-preserving reference 2D space, called the motion track representation. The principal advantage of this representation over standard representations of motion capture data - generally either a keyframed timeline or a 2D motion map in its entirety - is that it maps the motion differences along
the time dimension into parallel perceptible spatial dimensions but at the same time captures the primary content of the source data. Latent semantic differences that are difficult to be visually distinguished can be clearly displayed, favoring effective summary, clustering, comparison and analysis of motion database.

pdf video bibtex slides


Volume Exploration using Elliptical Gaussian Functions
Yunhai Wang, Wei Chen, Guihua Shang, Xuebin Chi

In Proceedings of IEEE Pacific Visualization Symposium, March 2010, Taibei
(Cover Image)

This paper presents an interactive transfer function design tool based on ellipsoidal Gaussian transfer functions (ETFs). Our approach explores volumetric features in the statistical space by modeling the space using the Gaussian mixture model (GMM) with a small number of Gaussians to maximize the likelihood of feature separation. Instant visual feedback is possible by mapping these Gaussians to ETFs and analytically integrating these ETFs in the
context of the pre-integrated volume rendering process. A suite of intuitive control widgets is designed to offer automatic transfer function generation and flexible manipulations, allowing an inexperienced user to easily explore undiscovered features with several simple interactions. Our GPU implementation demonstrates interactive performance and plausible scalability which compare favorably with existing solutions. The effectiveness of our approach has been verified on several datasets.

pdf video bibtex slides