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MIT CSAIL6.869: Advances in Computer Vision |
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Problem Sets
Please submit a hard copy of your work in class, and upload your code (and all files needed to run it, images, etc) to stellar.
| Posted | Due | ||
| Problem set 1: A Simple Visual System | February 2, 2011 | February 9, 2011 | pset1.pdf pset1_code.zip Solutions |
| Problem set 2: Filtering | February 9, 2011 | February 16, 2011 | pset2.pdf prob2.jpg Solutions |
| Problem Set 3: Statistical models of images | February 16, 2011 | February 23, 2011 | pset3.pdf pset3_code.zip Solutions |
| Problem Set 4: Color Constancy and Bayesian Inference | February 23, 2011 | March 2, 2011 | pset4.pdf TrainData.zip train_data2.mat (2/28/2001) Hints MAP solution by Hamed Alemohammad Contest leaderboard |
| Problem Set 5: 1-d BP and Textures | March 2, 2011 | March 9, 2011 | pset5.pdf curves.zip rings.jpg Solutions |
| Problem Set 6: Loopy BP and Image Segmentation | March 9, 2011 | March 16, 2011 | pset6.pdf tsukuba.zip pts.mat plot3dclusters.m sunset.bmp terrain.bmp Solutions |
| Problem Set 7: Anaglyph Camera Obscura | March 16, 2011 | March 30, 2011 | pset7.pdf Solutions Gallery |
| Problem Set 8: Image Mosaicing | March 30, 2011 | April 6, 2011 | pset8.pdf |
| Problem Set 9: Motion Magnification | April 6, 2011 | April 13, 2011 | pset9.pdf pset9_code.zip bookshelf.zip Solutions |
| Problem Set 10: Kalman Filter and Structure-from-Motion | April 14, 2011 | April 27, 2011 | pset10.pdf sfm.m Solutions |
| Problem Set 11: Recognition and Boosting | April 27, 2011 | May 4, 2011 | pset11.pdf faces.mat Solutions |
| Problem Set 12: Designing your talk | May 4, 2011 | May 9, 2011 | Answer the following questions from lecture: pset12.pdf |
Final Project
Project Proposal
Due: Mon April 4; upload to stellar.
The proposals should be just a page, and should describe what you plan to do (and who with, if appropriate). In the proposal, persuade us that it will be feasible for you to do it: lay out the tasks, and give a timeline for when you'll do each task. You can work by yourself or in pairs. Projects by pairs should be correspondingly more substantial.
Regarding the project topics: It should be something you're excited about. Anything related to computer vision is fine. We can help you with topics if you want ideas. We want it to be something new that you do for this class, so you can't submit a paper you've done for your RA or a project from another class. But something topically related to your RA is fine, and if it becomes a paper you submit for publication, that's ideal, of course.
Presentation
Due: Wed May 11 9:00am; upload to stellar.
The project presentation should be clear, informative, and short. You should briefly describe the problem you have chosen, and present an overview of your approach and results. The time allotted to each presentation is 5 minutes. We’ll have to be strict with the timing to accommodate all the students, so make sure your presentation fit within that time.
Submission: We will use one computer for the presentations in order to avoid the cost of everyone setting up their laptops. You should upload your presentation to stellar by the due time above as a single ppt or pdf file named <YOUR_LAST_NAME>.ppt (or .pdf). If your presentation has additional files (e.g. videos), upload it as a single zip file with the same naming convention. Late submissions are not allowed, and no further editing will be possible after submission.
Report
Due: Fri May 13; upload to stellar.
The report should be 5 - 8 pages (the upper limit of 8 pages is strict!) in CVPR format. It should be structured like a research paper, with sections for Introduction, related work, the approach/algorithm, experimental results, conclusions and references.
You should describe and evaluate what you did in your project, which may not necessarily be what you hoped to do originally. A small result described and evaluated well will earn more credit than an ambitious result where no aspect was done well. Be accurate in describing the problem you tried to solve. Explain in detail your approach, and specify any simplifications or assumptions you have taken. Also demonstrate the limitations of your approach. When doesn’t it work? Why? What steps would you have taken have you continued working on it? Make sure to add references to all related work you reviewed or used.
You are allowed to submit any supplementary material that you think it important to evaluate your work, however we do not guarantee that we will review all of that material, and you should not assume that. The report should be self-contained.
Submission: submit your report to stellar as a pdf file named <YOUR_LAST_NAME>.pdf. Submit any supplementary material as a single zip file named <YOUR_LAST_NAME>.zip. Add a README file describing the supplemental content. Late submissions are not allowed.