Suggestions for CSE 523/524 Projects
Here are some sample topic areas, that could
stimulate your choice of a narrower, more specific project. Please talk to me
about your ideas so that you can get help in focusing your work. Topics 1-4
were added Jan 2005
- Dynamic Probabilistic
Networks for Video analysis. The project would be to implement ideas
contained in the following papers, and test them either on video of
multiple activities, or on fMRI data (3D “movies” of the
brain): Gong, S. and T. Xiang. Recognition
of group activities using dynamic probabilistic networks. in IEEE International Conference on Computer Vision.
2003. or Vogler, C. and D. Metaxas.
Parallel hidden markov models for American sign language recognition. in
ICCV. 1999.
- Face Shape and Motion
Recovery: We have multiple research projects on this area. You will
work together with senior PhD students.
- You could work on the
new generation hardware and software forcapturing
human facial expression. Please talk to me for details.
- Implement: "Recovering
Shape and Reflectance Model of Non-Lambertian Ojects from Multiple Views", Tianli Yu, Ning Xu and Narendra Ahuja, CVPR04
- Implement:
"Real-Time
Combined 2D+3D Active Appearance Models", J. Xiao, Simon Baker,
L. Matthews and T.Kanade, CVPR04
- Facial
expression analysis and transfer using multinear
models. You would need to assist in the capture and analysis of data.
- Generalized
PCA (applied to fMRI data)
- Object Recognition and local
features:. Implement and compare the methods
described in the following papers:
- Object
class recognition using discriminative local features
- Weak
Hypotheses and Boosting for Generic Object Detection and Recognition
- Scale
and affine invariant interest point detectors
- Juggling
Tutor
- Virtual building Project,
joint work with Olaf Hall Holt. A number of
projects are available, talk to me for more details.
- Tracking objects using
"condensation". Tracking objects in cluttered backgrounds. More details.
- Bundle Adjustment: A method
that recovers both camera and scene geometry. Brief
description. Implement this method to
recover the camera geometries and a few feature points on sets of face
images. You would need to search the web to find a generic face model
first.
- Comparison of face
recognition techniques. You can implement at least three techniques and
compare them. You can start from this paper and the face recognition page.
- Implement Illumination-insensitive
Face Recognition Using Symmetric Shape-from-Shading
- Hidden
Markov Models and Gesture Recognition. Hidden Markov
Models have been succesfully applied to speech
recognition. Here we will explore their applicability to gesture
recognition.
- Vision and Learning. Learning
object models of non-rigid objects, for example. Learning image-based
representations. Active learning of shape or x. Implement
Support Vector Machines for face detection and recognition.