Tues/Thurs 11:00 am – 12:15 pm
CS 378, Unique # 54875
Instructor: Kristen Grauman
Email: grauman @ cs
Office location: CSA 114
Office hours: Wednesdays 5-6 pm, and by appointment.
TA: Jaechul Kim
Email: jaechul @ cs
Office hours: Mondays 3-4 pm, and Wednesdays 3-4 pm, in TAY basement computer lab.
TA: Yong Jae Lee (for office hours only)
Office hours: Tuesdays 4-5 pm, and Thursdays 4-5 pm, in TAY basement computer lab.
Please come to any of our office hours for questions about assignments or lectures.
Questions via email about assignments should be sent to: cv-fall2009 @ cs , with “378” in the beginning of the subject line.
This will help ensure a timely response from the instructor or TAs.
Answers for Section 1 of Pset 5 are available here.
Final exam is Monday 12/14, 2-5 pm in JGB 2.218. Example exams handed out in class on 12/1.
Check out class mosaic results here.
Check out class seam carving results here.
Use the course schedule for all reading assignments, deadlines, lecture notes, etc. Lecture slides are linked from the 2nd to last column.
Grades and late-days posted on eGradebook.
Billions of images are hosted publicly on the web---how can you find one that “looks like” some image you are interested in? Could we interact with a computer in richer ways than a keyboard and mouse, perhaps with natural gestures or simply facial expressions? How can a robot identify objects in complex environments, or navigate uncharted territory? How can a video camera in the operating room help a surgeon plan a procedure more safely, or assist a radiologist in more efficiently detecting a tumor? Given some video sequence of a scene, can we synthesize new virtual views from arbitrary viewpoints that make a viewer feel as if they are in the movie?
Computer vision is at the heart of many such questions: the goal is to develop methods that enable a machine to “understand” or analyze images and videos. In this introductory computer vision course, we will explore various fundamental topics in the area, including image formation, feature detection, segmentation, multiple view geometry, recognition and learning, and motion and tracking. An outline of the syllabus is here.
This course is intended for upper-level undergraduate students.
Basic knowledge of probability and linear algebra; data structures, algorithms; programming experience.
Previous experience with image processing or machine learning will be useful but is not required. Problem sets will include Matlab programming problems. There will be a Matlab tutorial in class on Thursday, September 3, and a warm-up assignment to get familiar with basic Matlab commands. We will recommend useful functions to check out per assignment. Students are expected to practice and pick up Matlab on their own in order to complete the assignments. The instructor and TAs are happy to help with Matlab issues during office hours.
If you are unsure if your background is a good match for this course, please come talk to me.
Problem sets: Problem sets will be given approximately every two weeks, and will involve a combination of concept questions and programming problems. The programming problems will provide hands-on experience working with techniques covered in or related to the lectures. All code and written responses must be completed individually. These problem sets will take time to complete; please start early!
Small assignments: In addition to the problem sets above, we may have a few very short assignments due within 3-7 days of their assignment.
Due dates: All problem sets are to be submitted by 11:59 PM on the day they are due unless otherwise noted on the assignment itself. Deadlines are firm, and we will not make exceptions. We will use the “turnin” program timestamp to determine time of submission. Anything from 1 minute to 24 hours is one day late (i.e., a timestamp of 12:00 AM or later is one day late).
The instructions in each problem set will designate which parts to submit electronically and which if any to submit via hardcopy. Any hardcopy portions should be submitted in class or left in Jaechul’s office, CSA 1.134. Create a pdf file for the report portion of the assignments. (The CS machines have openoffice which can be used to convert doc files to pdf if you work in Word and don’t have a Adobe PDF printer.)
Free late days (“slip days”): Over the course of the term you have an allowance of four free late days for problem set turn-ins, meaning you can accrue up to four days in late assignments with no penalty. Late problem sets beyond this allowance lose 50% of the total possible credit per day late. Please plan ahead so you can spend your late days wisely. No late problem sets will be accepted after solutions are discussed in class or posted online. Late days are timed as the assignments; we’ll count a full additional day as having passed for submissions 1 minute to 24 hours late. If you use any late days on an assignment, clearly include the number of days used at the top of your write-up.
Exams: There is an in-class midterm and a comprehensive final exam. For each exam students may use a single sheet (8.5 x 11”) of notes.
Participation/attendance: Regular attendance and participation in class is expected. If for whatever reason you are absent, it is your responsibility to find out what you missed that day.
Reading: The reading assignments listed on the schedule should be read before the associated class lecture.
Grading policy: Grades will be determined roughly as follows. You can check your current grades online at eGradebook.
· Problem sets (55%)
· Midterm exam (15%)
· Final exam (20%)
· Class participation, including attendance (10%)
Please read the UTCS code of conduct.
Please frequently check this page for course announcements, and use the schedule page for reading assignments, problem set downloads, deadlines, etc.
Midterm exam: Tuesday Oct 13 (in class) tentative
Last class meeting: Thursday Dec 3
Final exam: Monday Dec 14, 2:00-5:00 PM in JGB 2.218
recommended textbook is Computer Vision: A Modern Approach, by Forsyth and
Computer vision, Linda G. Shapiro and George C. Stockman.
Introductory techniques for 3-D computer vision, Emanuele Trucco and Alessandro Verri.
Multiple view geometry in computer vision, Richard Hartley and Andrew Zisserman.
· A script tattle-matlab.sh is posted on Blackboard->Course documents. You can use it to alert you when Matlab licenses are available, in case there’s another overload in the future. See the script for usage instructions. (Thanks to Jason Pepas for providing this.)
· OpenCV (open source computer vision library)
· Weka (Java data mining software)
· Object recognition databases (list compiled by Kevin Murphy)
· Various useful databases and image sources (list compiled by Alyosha Efros)
· Netlab (matlab toolbox for data analysis techniques, written by Ian Nabney and Christopher Bishop)
· Oxford Visual Geometry Group (contains links to data sets and feature extraction software)