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Curriculum
Vitae (pdf)
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Jingdan Zhang
PhD
Student
Department
of Computer Science
University
of
North Carolina
at
Chapel Hill
Address:
SN
365, Sitterson Hall, UNC-CH
Chapel
Hill
,
NC
27599
Phone:(919)
962-1976
Email:
zhangjd at cs.unc.edu
I am a fourth year
graduate student in Dept. of Computer Science at UNC-CH, working with Prof.
Leonard McMillan. My currently research area is computational
photography, image based rendering and modeling and computer Vision. I
am especially interested in applying probabilistic graphical models to a
variety of applications in above areas.
I got my master degree in
dept. of Computer Science and Application, Tsinghua
University (Beijing) in Summer 2003. I worked at Microsoft Research
Asia as an intern from 2002 to 2003, where I learned how to do research
by working with Xin
Tong, Baining
Guo and Harry
Shum.
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Robust
Tracking and Stereo Matching under Variable Illumination
Summary: Illumination
inconsistencies cause serious problems for classical computer
vision applications. We present a new approach to model
illumination variations using an Illumination Ratio Map (IRM).
The IRM is modeled as Markov network and be easily incorporated
into low-level vision problems, such as tracking and stereo
matching.
Jingdan Zhang, Jingyi Yu and
Leonard McMillan. Robust Tracking and Stereo Matching under
Variable Illumination. CVPR, 2006, Accepted.
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Data-Driven
Modeling of Mocap Data
Summary: Motion capture
data from human subjects exhibits considerable redundancy. We
exploit this redundancy by representing Mocap data with
piecewise local linear components, which are determined via a
divisive clustering method. This technique can be used to
predict the complete configuration of a human model based on a
subset of Mocap information as well as compressing and indexing
motion databases.
Guodong Liu, Jingdan Zhang, Wei
Wang and Leonard McMillan. A system for analyzing and
indexing human motion databases (demo). Proc. ACM SIGMOD
International Conference on Management of Data (SIGMOD),
924-926, 2005. (pdf)
Guodong Liu, Jingdan Zhang, Wei
Wang and Leonard McMillan. Human Motion Estimation from a
Reduced Marker Set. To appear ACM SIGGRAPH Symposium on
Interactive 3D Graphics (I3D), 2006.(pdf)
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Progressively-Variant Texture
Synthesis
Summary:
We present an approach for decorating surfaces with
progressively-variant textures. A progressively-variant texture
can model local texture variations, including the scale,
orientation, color, and shape variations of texture elements. We
developed techniques for modeling progressively-variant textures
in 2D as well as for synthesizing them over surfaces.
Jingdan Zhang,
Kun Zhou, Luiz Velho, Baining Guo and Heung-Yeung Shum. Synthesis
of Progressively-Variant Textures on Arbitrary Surfaces. ACM
Transactions on Graphics(Proc. ACM SIGGRAPH), 295-302, 2003.
(pdf)
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Synthesis
and Rendering of Bidirectional Texture Functions on Arbitrary
Surfaces
Summary:
The bidirectional texture function (BTF) is a 6D function that
can describe textures arising from both spatially-variant
surface re-flectance and surface mesostructures. For both BTF
synthesis and hardware-accelerated rendering, a main challenge
is handling the large amount of data in a BTF sample. In this
project, we developed algorithms to synthesize BTF to arbitrary
surfaces and render the synthesized BTF with GPU
acceleration.
Xin
Tong, Jingdan Zhang, Ligang Liu, Xi Wang, Baining Guo and Heung-Yeung
Shum. Synthesis of Bidirectional Texture Functions on
Arbitrary Surfaces. ACM Transactions on Graphics(Proc. ACM
SIGGRAPH), 665-672, 2002. (pdf)
Xinguo
Liu, Yaohua
Hu, Jingdan Zhang, Xin
Tong, Baining
Guo and Heung-Yeung
Shum. Synthesis and Rendering of Bidirectional
Texture Functions on Arbitrary Surfaces. IEEE
Transactions on Visualization and Computer Graphics,
10(3): 278-289, 2004.(pdf)
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3D
Model and Texture Acquisition From Stereo Images
Summary:
This project is related to my master thesis. I have built a
prototype system that can automatically reconstruct the
geometric as well as texture information from real objects. In
this system, a four-freedom robot controls the position and
orientation of an object and I use stereo vision and structured
light to recovery geometric information of the object.
Jingdan Zhang, Realistic
Modeling Techniques Based On Real-World Sampling Dataset,
Master's degree thesis, June. 2003. (Abstract)
(pdf,
Chinese)
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Three-Dimensional Biomedical
Image Interpolation
Summary:
We
present a novel three-dimensional gray-level interpolation
method called Directional Coherence Interpolation (DCI). DCI
interpolates the missing image data along the maximum coherence
directions (MCD), which are estimated from the local image
intensity yet constrained by a generic smoothness term. The
principal advantage of the proposed approach is that it leads to
significantly higher visual quality in 3D rendering when
compared with traditional biomedical image interpolation
methods.
Jingdan Zhang, Yongmei Wang and
Baining Guo. Pyramidal Search of Maximum Coherence Direction
for Biomedical Image Interpolation. IEEE International
Symposium on Biomedical Imaging, 887-890, 2002.(pdf)
Yongmei Michelle
Wang, Jingdan Zhang, Zhunping Zhang, Baining Guo. Directional
Coherence Interpolation for Three-Dimensional Gray-Level Images.
International Journal of Image and Graphics, 4(4), 535-561,
2004.(pdf)
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Image Segmentation for image
retrieval system
Summary:
We propose a algorithm efficiently combining the local and
global information to achieve unsupervised segmentation of color
images.
Jingdan Zhang,
Zhidong Deng, Baining Guo. Two Stage Unsupervised
Segmentation of Color Images. Proc. Chinagraph, 144-148,
Beijing, Sept 2002. (Abstract)
(pdf, Chinese)
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High quality texture mapping
Summary: We
describe a new method to map a texture on a surface with a
spatially-variant filter. Our filter takes into consideration
the effects of anisotropy using a Jacobian approximation while
computing the sampling rate, and the interpolation weights are
computed with a sinc function.
Ke Deng, Jingdan
Zhang, Lifeng Wang and Baining Guo. Texture Mapping with a
Jacobian-Based Spatially-Variant Filter. Proc. IEEE Pacific
Graphics, 2002.(pdf)
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