Course schedule :: Annoucements & Resources
The course surveys recent developments in high level computer vision such as object recognition and categorization, object tracking and human motion analysis, spatial and temporal reasoning for scene reconstruction and understanding as well as some elements of robotics and mobile vision. The course also explores recent machine learning techniques such as graphical models and inference algorithms for tackling high level visual tasks.
- Present 1-2 set of papers
- Read papers and participate at class discussion during paper presentations
- Course project: replicate existing methods or implement new research ideas.
- Class participation & discussion: 20%
- Paper presentation (quality, clarity, depth, etc.): 30%
- Course project (quality of the project presentation, work, writing, etc): 50% ( progress report 5%; final report 35%; presentation 10%)
- Project late policy: 25% if one day late; 50% if two days late; zero credit if more than two days
Knowledge of linear algebra and probability are necessary for understanding the material covered in this class. Some knowledge of computer vision is desirable but not required. MATLAB or equivalent programming experience is desirable.