Description
This course introduces techniques for Computer Vision. The course presumes a reasonable background in calculus and linear algebra; probability would be an asset as well. The course is really made up of three units. The first considers image formation and processing as is immediately relevant to computer vision (we will try not to cover material best left for an Image Processing course) The second unit covers fundamental computer vision, the science of recovering information about the world from images. This work includes shape recovery, object recognition, and video understanding. Finally we will also cover interesting applications and some of the particular research here at CoC - after all, we have to have some fun too.
Instructor
|
Aaron Bobick |
Teaching Assistant
Alexander Stoytchev
Email: saho@cc.gatech.edu
Phone: (404) 894-9311
Office Hours: Wed. 2:00-3:30 pm; or by appointment.
Location: CCB Picnic Area
The first draft of the syllabus for this course can be found here. As the course solidifies we will generate a lecture by lecture table.
The lectures for this class are being captured.
To find them go to eClass index and click on Computer Vision. (soon to be
there!)
Slides from Jim Rehg's lectures (in Zip Format).
The grades will be assessed as follows:
|
Problem Sets (not all PS are created equal) |
50% |
|
Mid-term |
15% |
|
Reading critiques ([almost] All or none!) |
5% |
|
Final project |
20% |
|
Class Participation |
10% |
There will be 4 or so bi-weekly problem sets that will involve some Matlab and hopefully some thinking. Collaboration on problem sets is encouraged at the "white board interaction" level. That is, share ideas and technical conversation, but write your own code. All problem sets should be in on time. One late problem set is accepted late (but before the next one is due) without excuse. After that, get prior permission.
There will be a short mid-term (one hour) just to make sure we're all on the same page.
There will be occasional readings assigend. You will also be given a single question to answer and will be expected to hand in a less than full page answer (a paragraph may often suffice). If you are missing more than two critiques you lose the whole 5%. I.e. we just really want you to read these papers!
Undergrads and grads will be graded on separate curves; more is expected from a graduate project than an undergraduate project.
The text for this course is Image Processing, Analysis, and Machine Vision by Sonka, Hlavac, and Boyle. Our sources tell us it is in the bookstore. As you will see we have selected this book because of the topics it covers not how it covers the topics.
There will be additional required readings. When they are available electronically you will be able to find them here:
Sept. 4: The Laplacian Pyramid as a Compact
Imgage Code
Missing page 13 from Bergen and Hingorani's paper "Hierarchical Motion-based Frame Rate Conversion".
Problem sets will be posted here.