1. Today

2. Next week

3. Class project Do a research project Write it up as a conference paper
Using CVPR, NIPS, or SIGGRAPH styles.
Read and review someone else’s class project paper.
Auditors are welcome to participate in this.

4. Dates
I’d like you to have your projects selected and approved before MIT spring break, so:
Please me project proposals by Monday, March A paragraph describing the project, why it’s interesting to you, and why it will probably work out.
Wednesday, May 8: final project due.
Wednesday, May 15: your review of someone else’s final project due.

5. Possible projects Something from your own research.
Do something new and interesting with techniques we covered in class (or one we might have covered).
Evaluate the performance of an existing algorithm.
Invent a new technique or algorithm.

6. Something from your own research
Should be related to vision and learning.
Have a self-contained paper done by May 8.
If the research is independent of this class, then the paper should be very well written.

7. Do something with a technique from class
Combine manifold learning and dynamic textures (learn the dynamics on some manifold that you learn).
Collect a great database and analyze it.
E.g. digitize 500 bird images, choose an image metric, and infer a hierarchical categorization of those images. Or use SVM’s or boosting for some discrimination task.
Apply Frey and Jojic to web cam data.
Verify that Jepson/Fleet paper really works as well as it seems to.

8. Evaluate the performance of an existing algorithm
Do psychophysical evaluation of, e.g., super-resolution algorithm.
Compare subjects’ preference for linear interp, cubic spline, and machine learning approaches.
Compare belief propagation with conjugate gradient descent and other algorithms for some minimization problems.
Verify that Y. Weiss’s intrinsic images algorithm recovers the correct answer
Use real objects of known albedo, or use high quality computer graphics renderings, with everything known. Evaluate algorithm performance under different conditions.

9. Invent a new technique or algorithm
Warning: slide contains some half-baked ideas.
Extend style/content analysis to unsupervised case. Or to a non-parametric version.
Develop motion analysis algorithm for an object moving through variable illumination (under shadows) exploiting Weiss’s intrinsic image observations.
Develop a dynamical model exploiting a translation-based image representation. Or which uses earth-mover’s distance as an image metric.

10. Relevant publication venues
IEEE PAMI special issue Graphical Models in Computer Vision, see link from my web page,
June 24, 2002 deadline
Neural Information Processing Systems (NIPS), www2.cs.cmu.edu/Groups/NIPS/
July 1, 2002 submission deadline
Computer Vision and Pattern Recognition (CVPR),
Fall, 2002 submission deadline
SIGGRAPH (the graphics conference)
January, 2003 deadline