Tues/Thurs 12:30 – 2:00 pm
Parlin Hall 1 (PAR, down the stairs from the front entrance)
CS 378, Unique # 56705 (undergrads)
CS 395T, Unique# 56850 (grads)
Instructor: Prof. Kristen Grauman
Email: grauman – put the at sign – cs.utexas.edu
Office hours: Thurs 2:00-4:00 pm in TAY 4.118 (or by appt)
TA : Sudheendra Vijayanarasimhan
Email: svnaras – put the at sign -- cs.utexas.edu
Office hours : Mon 1 :00-2 :00 pm, Wed 12:00-1:00 pm in ENS 31NQ
The TA station is in the basement of ENS inside room 31NR. Directions to the TA stations are posted right outside the basement elevator, and also outside room 31NR.
This is the 2007 course website. The Fall 2008 Computer Vision site is here.
Outline of course topics for review.
Grad student assignments due 12/6: submit hardcopy.
See current reading assignments on the schedule.
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 cross-listed for upper-level undergraduate (CS 378) and graduate (CS 395T) students. Additional work is required of graduate students (see below).
Basic knowledge of probability and linear algebra; data structures, algorithms; programming experience.
Previous experience with image processing, machine learning, and statistics will be useful but is not required. Problem sets will include some Matlab programming.
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. Students are encouraged to discuss the problem sets and brainstorm about solutions together, but all code and written responses must be completed individually.
Due dates: All problem sets are to be submitted before class on the day they are due. The instructions in each problem set will designate which parts to submit electronically and which to submit via hard copy.
Over the course of the term you have an allowance of three free late days for problem set turn-ins, meaning you can accrue up to three 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 given in class or posted online.
Exams: There is an in-class midterm quiz and comprehensive final exam.
Participation: Regular attendance and participation in in-class activities is expected. If for whatever reason you are absent, it is your responsibility to find out what you missed that day.
Additional requirements for graduate students: In addition to the problem sets and exams, graduate students will also be expected to complete one research paper review and one problem set extension throughout the course of the term. Details will be discussed in class.
Grading policy: Grades will be determined roughly as follows. For graduate students, the additional problem set extension and paper review requirements factor into the first component.
· Problem sets (50%)
· Midterm quiz (15%)
· Final exam (20%)
· Class participation (15%)
Please read the UTCS code of conduct.
Midterm exam: Tuesday Oct 9, in class
Last class meeting: Thursday Dec 6
Final exam: Thursday Dec 13, 9:00-12:00
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.
· 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)
· Matlab tutorials and quick references: