About Me
I am Quan Wang, a third-year Ph.D. student majoring
in Computer & Systems Engineering at RPI.
My advisor is Professor Kim L. Boyer.
My research interests focus on:
My resume: click here
Email: wangq10@rpi.edu
Lab address:
JEC 6304
Signal Analysis and Machine Perception Laboratory (SAMPL)
Department of Electrical, Computer, and Systems Engineering
Rensselaer Polytechnic Institute
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I have been working with Prof. Kim L. Boyer to develop a novel approach,
the Active Geometric Shape Models, to fit parametric shapes to data and images.
Our paper is published on CVIU.
We approach the cell tracking
problem by interpreting it as a classification problem.
Our paper is published on ICPR 2012.
We are starting a new project to develop both hand-designed and unsupervised learning-based features to detection composition styles in photographs. 
This is a project I have been working on when I was in Prof. Badrinath Roysam's lab.
My work is part of the 
This is my research for undergraduate thesis at Tsinghua University, under the guidance of Prof. Qionghai Dai
and Prof. Guihua Er.
This is the final project of Prof. Qiang Ji's course Introduction to Probabilistic Graphical Models.
In this project, we first study the Gaussian-based hidden Markov random field (HMRF) model and its expectationmaximization (EM) algorithm.
Then we generalize it to Gaussian mixture model-based hidden Markov random field. The algorithm is implemented in MATLAB.
We also apply this algorithm to color image segmentation problems and 3D volume segmentation problems.
This is the final project of Prof. Birsen Yazıcı's course Detection and Estimation Theory.
In this project, we study the hidden Markov random
field (HMRF) model and its expectation-maximization (EM)
algorithm. We implement a MATLAB toolbox named
HMRF-EM-image for 2D image segmentation using the
HMRF-EM framework. This toolbox also implements edge-prior-preserving
image segmentation, and can be easily reconfigured
for other problems, such as 3D image segmentation.
This is the final project of Prof. Qiang Ji's course Pattern Recognition.
In this paper, we discussed the theories of PCA,
kernel PCA and ASMs. Then we focused on the pre-
image reconstruction for Gaussian kernel PCA, and
used this technique to design kernel PCA based ASMs.
We tested kernel PCA on synthetic data and human
face images, and found that Gaussian kernel PCA
succeeded in revealing more complicated structures of
data than traditional PCA and achieving much lower
classification error rate. We also implemented the
Gaussian kernel PCA based ASMs and tested it on
human face images. We found that Gaussian kernel
PCA based ASMs is promising in providing more
deformation patterns than traditional ASMs.
This is the final project of Prof. Qiang Ji's course Computer Vision.
In this project, we established a framework to convert 2D videos to pseudo 3D videos.
Our basic idea is to track the moving objects in the video and separate them from the background.
Then we give different depth information to the objects and the background, and visualize them in 3D.
