Yang Bai (白杨)
Ph.D. Candidate
Advanced Imaging and
Collaborative
Information Processing (AICIP) Lab
Min H. Kao Department of Electrical
Engineering and Computer Science
University of Tennessee, Knoxville
536
Science
and
Engineering
Research
Facility (SERF)
1414
Circle
Drive
Phone: (865)974-6334 (Office)
(865)964-7303 (Cell)
I'm currently
looking for a full-time R&D position in computer vision, digital
image processing, machine learning, wireless sensor networks and
related. Here is my resume.
I'm a Ph.D. student in the department of EECS at the University of Tennessee, Knoxville. My advisor is Dr. Hairong Qi. I received my B.E. and M.S. in Electrical Engineering from Beijing Institute of Technology, Beijing, China, in 2004 and 2006, respectively. I came to the University of Tennessee to pursue my Ph.D. degree in 2006 and worked at the Advanced Imaging and Collaborative Information Processing (AICIP) Lab since 2007. I will graduate in Spring 2011 with a Ph.D. degree in Computer Engineering.
Research
Interests
My
research
interests
include
computer
vision,
digital imaging processing, machine learning, and wireless
visual sensor networks. I'm
extensively involved in projects in terahertz image processing, feature
- based image comparison, and energy
conservation for wireless visual sensor networks.
Awards
Extraordinary Professional Promise Award, University of
Tennessee, Knoxville, 2010.
Best Thesis Awards, top 5%, Beijing Institute of Technology, 2006.
Renmin Scholarship, top 10%, Beijing Institute of Techology, 2000 -
2004.
Projects
Feature-based
image
comparison
algorithm
design
for
wireless
visual
sensor
networks
The Wireless Visual Sensor Network
(WVSN) is a group of networked visual sensor nodes with image/video
capturing, on board computing and wireless communication capabilities.
Although each individual node is build at low cost and its capability
is severely limited, working together as a whole, the WVSN can achieve
high level computer vision tasks such as environment surveillance,
target tracking and object recognition, etc. In order to collaborate
with peer nodes, the sensors should form into clusters so that within
each cluster all sensors could capture the similar or the same scene.
Cluster forming for WVSN should utilize both geographical distance and
the Field of View (FOV) information of each visual sensor, because the
visual sensor is directional and the visual occlusions are ubiquitous.
Our solution for the
cluster forming relies on the feature-based image comparison algorithm
where each sensor compares the features of the image captured by its
own and its geographical neighbor sensors. Direct image comparison can
discover the visual sensors that are capturing similar or the same
scene, but raw image transmission requires high bandwidth and
consumes a lot energy, both of which are scarce resources for a WVSN.
Feature-based image comparison provides a more compact way for
representing images by using image features, which are more economical
in terms of data transmission. We proposed a light-weight feature point
(corner) detector and found through thorough comparisons that the Scale
Invariant Feature Transform (SIFT) descriptor performs best among all
other feature descriptors, including the Moment Invariant and the
Speeded-Up Robust Feature (SURF) descriptor.
Terahertz Image Processing
Because of their unique capabilities, terahertz waves have demonstrated
great potential in the concealed objects detection. However, the
limitation in terahertz wave generation/detection devices and the
disturbance from the pervasive water molecule absorption have hindered
its wide deployment. Terahertz image reconstruction refers to the
technique that extract useful information from raw data to produce a
meaningful image.
We define the term "differential spectrum" as the difference between
the transmitted spectrum and the received spectrum of the terahertz
wave, and model the overall absorption effect as a linear combination
of the differential spectrum of individual substance. Then a linear
unmixing algorithm can be used to separate the overlapped substances
represented by their weights, producing several image planes each
associated
with one of the substances in the image scene.
Experiences
Engineering Intern, 05/2009 - 08/2009 Aldis Inc., Oak Ridge, TN
Teaching Assitant, 08/2006 - 05/2010 Min H. Kao Dept. of EECS, University of Tennessee, Knoxville
Publications
[2] Yang Bai and Hairong Qi, “An effective approach to corner point detection through multiresolution analysis,” submitted to IEEE International Conference on Image Processing (ICIP), 2011.
[3] Yang Bai and Hairong Qi, “Feature-based image comparison for semantic neighbor selection in resource-constrained visual sensor networks,” EURASIP Journal on Image and Video Processing, special issue on Multicamera Information Processing: Acquisition, Collaboration, Interpretation, and Production, pp. 1-12, 2010.
[4] Yang Bai and Hairong Qi, “Redundancy removal through semantic neighbor selection in Visual Sensor Networks”, in Proceedings of the Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), pp. 1-8, 2009.
[5] Yang Bai and Hairong Qi , “A new perspective on terahertz image reconstruction based on linear spectral unmixing”, in Proceedings of the 15th IEEE International Conference on Image Processing (ICIP), pp. 1-4, 2008.
ECE692: Advanced Image Processing (Spring 2010)
Math 571: Numerical Analysis I (Fall 2009)
ECE 553: Computer Networks (Fall 2008)
ECE 517: Reinforcement Learning (Fall 2008)
Math 576: Linear Programming (Spring 2008)
ECE 618: Nonlinear Programming II (Spring 2008)
Math 523: Probability (Fall 2007)
ECE 692: Advanced Signal Processing (Fall 2007)
ECE 617: Nonlinear Programming I (Fall 2007)
ECE 571: Pattern Recognition (Spring 2007)
ECE 506: Digital Signal Processing II (Spring 2007)
ECE 572: Digital Image Processing (Fall 2006)
ECE 505: Digital Signal Processing I (Fall 2006)