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Project :
The goal of this project is to study a state-of-the-art topic. You can do this by either implementing a recent paper, or by coming up with your own idea of a cool application.
A good way to collect ideas is by going over papers published in one of the leading computer vision conferences (ICCV,CVPR or ECCV). Here is a link to all the papers of recent years:
http://www.cvpapers.com/index.html
Project proposal
You must send me a project proposal (at most 2 pages long) describing the project you’ve selected. The project proposal should include the following:
- Paper details: Title, author names, where was the paper published.
- Abstarct: a short summary of the problem you wish to solve.
- Background: why is ths problem relevant and interesting.
- Approach: the solution direction you intend to adopt.
- Experiments: what experiments you plan to do and on what data.
- References: list of related papers.
Final submission
- Project summary
Your project summary should include the following:
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- Paper details: Title, author names, where was the paper published.
- Abstarct: a short summary of the problem you solved.
- Background: why is ths problem relevant and interesting.
- Approach: the solution direction you adopted.
- Experiments: what experiments you did and on what data.
- References: list of related papers.
- Conclusions: This should include the following:
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- What worked?
- What didn’t work?
- What was the simplest step?
- What was most difficult?
- Have you changed any of the initial decisions?
- What would you have done differently if you had to do it again?
- How do you expect this to improve your results?
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- Code+data
Your submission should include:
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- A “help” description for each function.
- Coments explaining non-trivial steps in the code.
- runme.m function that will be called and the code will run
- Data required to run the code.
Here are a few papers I have selected (no need to stick to this list)
Topic | Paper |
Fun with images | Optimizing Content-Preserving Projections for Wide-Angle Images Robert Carroll, Maneesh Agrawala, Aseem Agarwala SIGGRAPH’09 |
Content-Preserving Warps for 3D Video Stabilization Feng Liu, Michael Gleicher, Hailin Jin, Aseem Agarwala SIGGRAPH’09 |
|
Filter Flow (PDF, supplemental material)Steven M. Seitz, Simon BakerICCV’09 | |
Image matching | SIFT flow: dense correspondence across different scenes Ce Liu, Jenny Yuen, Antonio Torralba, Josef Sivic, William T. Freeman ECCV’08 |
Scale & Affine Invariant Interest Point Detectors K Mikolajczyk & C. Schmid, IJCV’04 |
|
Linear Solution to Scale and Rotation Invariant Object Matching Hao Jiang, Stella X. Yu CVPR’09 |
|
Multi-camera action recognition | Multi-Camera Activity Correlation Analysis Chen Change Loy, Tao Xiang, Shaogang Gong CVPR’09 |
Free viewpoint action recognition using motion history volumes D Weinland, R Ronfard, E Boyer – Computer Vision and Image Understanding, 2006 |
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Motion history volumes for free viewpoint action recognition D Weinland, R Ronfard, E Boyer – IEEE International Workshop on Modeling People and Human , 2005 |
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Action recognition | Recognizing Realistic Actions from Videos “in the Wild” Jingen Liu, Jiebo Luo, and Mubarak Shah CVPR’09 |
Actions in context (project page) Marcin Marszalek, Ivan Laptev, Cordelia Schmid CVPR’09 |
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Video summarization | Understanding Videos, Constructing Plots – Learning a Visually Grounded Storyline Model from Annotated Videos Abhinav Gupta, Praveen Srinivasan, Jianbo Shi, Larry S. Davis CVPR’09 |
Saliency detection | |
Image features | Compact Signatures for High-Speed Interest Point Description and Matching (PDF)Michael Calonder, Vincent Lepetit, Pascal Fua, Kurt Konolige, James Bowman, Patrick MihelichICCV’09 |