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Four major
research areas:
Some research
projects:
- Pervasive Visualization
- Next Generation Visualization
System
- Vascular Image
Visualization
- Perceptually-Based Comparison
of Direct Volume Rendered Images
- Network Visualization: Visualizing
China
Webpages
- GPU-Assisted Visualization
Using O-Buffers
Pervasive
Visualization: Visualization on Mobile
Devices
Project Description: Visualization is a
very powerful tool for physicians, scientists, engineers, etc. to gain
insight into their data using computer graphics and imaging
techniques. Traditionally, the data can only be visualized with high-performance computers
in fixed locations. This limits the applications of visualization.
With the
advent of high-bandwidth wireless networks and pervasive computing
environments, the space and platform barriers for visualization are being
broken. In this proposal, we introduce pervasive visualization, which will
allow users to visualize data anywhere, anytime, on various mobile devices
connected by wireless networks. This research will address some key issues related
to pervasive visualization. We will investigate how to achieve meaningful
visualization on mobile devices with limited resources such as PDAs and cell phones. We will investigate using mobile
devices both as standalone visualization platforms and as platforms closely
collaborating with other devices in a pervasive computing environment. We
will further explore new applications of pervasive visualization in medical
imaging, education, and navigation of virtual environments.
The
results of our research will make visualization more accessible and more
powerful. Physicians, scientists, engineers, students, and other
visualization users will benefit from our research.
Network
Visualization: Visualizing Webpages in China
Project Description: Network
visualization is the use of interactive computer graphics and imaging techniques to
help users gain insights into massive data whose internal relationships can
be described using networks or graphs. Hyperlinks among Internet webpages and citations in scientific papers are two
typical examples. The rapid growth in the size and complexity of these data have
made network visualization a very important and challenging problem for
information processing.
In this
project, we will develop new network visualization techniques that can
scale well even for extremely large data. We will investigate texture-based
techniques for multivariate data visualization by exploiting the recent
development in controllable texture synthesis. We will explore the
application of scientific visualization techniques such as flow visualization
and volumetric methods in network visualization. New 3D visual metaphors
such as layered 3D highways and clustered wires will also
be investigated. We will integrate all these techniques into one
real system that will be used to visualize two
terabytes data related to webpages in mainland China and Hong Kong collected by Peking University. Our research will benefit millions of
Internet users and have important academic and commercial applications.
Vascular Image
Visualization
 
Project Description: Vascular diseases
have become an important health issue in recent years. Direct volume
rendering is an effective way to visualize 3D vascular images for diagnosis
of different vascular pathologies and planning of surgical treatments.
Angiograms are typically noisy, fuzzy, and contain thin vessel structures.
Therefore, some kind of enhancements is usually needed
before direct volume rendering can start.
However, without visualizing the 3D structures in angiograms, users
may find it difficult to select appropriate parameters and assess the
effectiveness of the enhancement results. Also,
traditional enhancement techniques cannot easily separate the vessel voxels from other contextual structures with the same
or very similar intensity. In this thesis, we propose a framework to
integrate enhancement and direct volume rendering into one visualization
pipeline using multi-dimensional transfer function tailored for visualizing
the curvilinear and line structures in angiograms. Besides, as we found
that rendering of small vessels is problematic using conventional
approaches, we present a feature-preserving interpolation method to render
very thin vessels that are usually missed in
traditional approaches. Furthermore, in order to increase the effectiveness
and illustrative power of visualization, we introduce several
non-photorealistic rendering methods into our system. Our goal is to effectively convey the essential information about
the image by presenting the structures in different manners using different
rendering styles.
Perceptually-Based
Comparisons of Direct Volume Rendered Images
  
Project Description: Direct volume
rendering (DVR) is a widely used technique in visualization. There are
various DVR methods, such as ray casting, splatting,
2D texture slicing, and 3D texture slicing. The images generated by these
methods are somewhat different. Even with the same DVR method, different
rendering parameter and algorithm setting also produces different images.
As the direct volume rendered images will be perceived by
human beings, it is interesting to quantitatively find out whether
the visible differences between two images will be observed. In this
project, we apply a perceptually-based comparison
metric, which is based on Visible Differences Predictor (VDP) developed by
Daly, to systematically compare the direct volume rendered images. We also use
this metric to investigate the alpha threshold value for early
ray-termination in a ray-casting algorithm. Experimental results
demonstrate that our approach provides an effective way to evaluate the
quality of directed volume rendered images. A new perceptually-based
acceleration technique for DVR can be developed based on our work.
GPU-Based
Visualization Using O-Buffers
Project Description: In recent several
years, the GPU (graphics processor) on commodity video cards has evolved
into a very flexible and powerful processor, which provides both
vertex-level and pixel-level programmability. The state-of-the-art GPUs are much faster than CPUs and are getting faster
and faster. More importantly, GPUs are
inexpensive and ubiquitous nowadays. Therefore, the GPU will have a huge
impact on computer graphics, visualization, and simulation. This research will investigate
how to exploit the GPUs’s newly available
flexibility and processing power to accelerate visualization. The core task
is to use the GPU to accelerate the rendering of the O-buffer, which is a
framework we proposed for sample-based graphics and visualization. Visualization
has become a very important tool in scientific computing and medical
imaging. Our research will significantly improve the visualization speed of
large scientific and medical data and will greatly facilitate the ability
to explore even larger data.
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