MIT CSAIL6.869: Advances in Computer Vision
|TA||Aditya Khosla (Office hours: Tuesdays, 17:00-18:00, 24-322)
|Course mailing firstname.lastname@example.org|
|Lecture||TR 1:00PM - 2:30PM (4-163)|
|Units||3-0-9 (Graduate H-level, Area II AI TQE)|
|Prerequisites||6.041 or 6.042; 18.06|
Sept 7, 2013: Office hours
Aditya's office hours will be on Tuesdays 17:00-18:00 at 32-D428 starting from this week, with the first one on Sept 10th, 2013.
Sept 5, 2013: Piazza
We created a Piazza for student discussion. All the course materials will still be hosted on this website, and you still have to submit your homework through Stellar. The Piazza student discussion is totally optional and will not be used as an evaluation for grades: https://piazza.com/mit/fall2013/6869/home
Sept 5, 2013: Stellar
The Stellar website is open for assignment submission.
Sept 5, 2013: Matlab Tutorial
We will have a Matlab Tutorial for people with little Matlab experience. If you know how to program in Matlab well, you don't need to attend this tutorial.
Time: 3pm-4pm, Sept 6 (Friday).
Sept 5, 2013: Welcome to 6.869!
Make sure to check out the course info below, as well as the schedule for what's up ahead.
Good luck with your semesters!
This course covers fundamental and advanced topics in computer vision with a focus on image statistics, machine learning techniques, and applied vision for graphics. Topics include image representations, frequency analysis, texture models, shape-from-X algorithms, Bayesian inference, object and scene recognition, motion estimation and tracking, multi-view geometry, and image databases. Covers topics complementary to 6.801/6.866; these subjects may be taken in either order.
The assignments in this class are comprised of problem sets and a final project. There are no exams or quizzes. Problem sets will be handed out on (almost) weekly basis. See the schedule for more details. Assignments will be posted online and are due in class by the end of the specified day's lecture. All assignments must be handed in. Grades will be given on a discrete 1-5 scale, where 1 is marginal performance and 5 is good performance.
The assignments are designed to give you both theoretical and practical experience with the material discussed in class. Since computer vision is an applied research field, parts of the assignments will involve programming and experimentation. Those are generally designed to be carried out in MATLAB. Although we allow you to use any coding environment that is convenient for you, we highly recommand MATLAB because it is the most popular language used in computer vision research community. We care more about the report than the actual code.
You should submit a hard copy of your work in class, and upload your code (and all files needed to run it, images, etc) to stellar. Solutions will be posted on the class website one week after the assignment is due.
Late Policy. You have upto 6 late days for all assignments in the semester and you can use them at your discretion (don't use them needlessly). Any additional unapproved late submission will be considered as unsubmitted work. Late submission is not allowed for the final project and proposal.
Collaboration Policy. We allow discussing problem sets with one or two classmates, but you must submit your own write-up and list your collaborators. You are allowed to collborate with one more student for the final project.
The final project will allow you to explore in depth a topic covered in class which you found interesting and like to know more about. During the semester we will propose ideas for projects in the problem sets and lectures, and we also encourage the students to come up with their own ideas that entice them. The topic for the final project and its scope should be approved by the class staff.
Overall, the final project is comprised of (a) a project proposal, (b) a five-minute class presentation, and (c) a report documenting your work, results and conclusions. More details on each of these milestones will be given as its deadline approaches.
Grading will be based on our assessment of your understanding of the class material, and will be roughly comprised of:
Contribution to the class discussion will also be taken into account.
A set of class notes which will be available on this website before each lecture. Additional recommended books and resources are listed in the course materials page. For those interested in further readings - the notes contain relevant references, and we will also post links to additional related papers before each class.