Computer Vision
Fall 2008
Tues/Thurs
12:30 – 2:00 pm
Parlin Hall 1 (PAR 1)
CS 378, Unique # 55770
Instructor: Prof. Kristen Grauman
Email: grauman @ cs
Office hours: Wednesdays 1:00-2:00 pm,
Thursdays 2-3 pm in CSA
114
TA : Harshdeep Singh
Email:
harshd @ cs
Office hours : Tuesdays 3:30-4:30 pm, Fridays 2-3 pm in TAY CS Lab.
Schedule Blackboard Overview Requirements Links Syllabus
outline eGradebook
Announcements
Pset 4 hardcopy turnin: either in class on
12/4, otherwise drop in CS homework drop box (put course name at top).
Use
the class schedule
to view all reading assignments, deadlines, and problem sets.
Grades,
late days used available on eGradebook.
Overview
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.
Prerequisites
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.
If
you are unsure if your background is a good match for this course, please come talk to me.
Course requirements
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 assignment, but all code and written responses must be completed
individually.
Due dates: All
problem sets are to be submitted by 11:59 PM on the day they are due. 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 CSA 114.
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.)
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 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. Notify Harshdeep
via email (harshd@cs) by the due date if you will be
using any late days.
Exams: There is an
in-class midterm quiz and a comprehensive final exam. For each exam students may use a single sheet
(8.5 x 11”) of notes.
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.
Grading policy: Grades will
be determined roughly as follows.
·
Problem sets (55%)
·
Midterm quiz (15%)
·
Final exam (20%)
·
Class
participation, includes attendance (10%)
Please read the UTCS code of conduct.
Important dates
Midterm
exam: Tuesday Oct 14 (in class) tentative
Last
class meeting: Thursday Dec 4
Final
exam: Saturday Dec 13, 7:00-10:00 PM in UTC
3.104
Books
The
recommended textbook is Computer Vision: A Modern Approach, by Forsyth and
Computer vision
: a modern approach, David A. Forsyth and Jean Ponce.
Computer
vision, Linda G. Shapiro and George C. Stockman.
Introductory
techniques for 3-D computer vision, Emanuele Trucco and Alessandro Verri.
Computer vision, Dana H. Ballard and Christopher M. Brown. (available online)
Multiple
view geometry in computer vision, Richard Hartley and Andrew Zisserman.
Pattern
classification, Richard O. Duda, Peter E. Hart, and
David G. Stork.
Machine
learning, Tom M. Mitchell.
Links
·
UTCS Computing
/ Facilities web page
·
For remote
access: Run an X server for Windows (such as XMing),
then login to your CS account with an SSH client (like Putty)
·
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)
·
Annotated
computer vision bibliography
·
Computer vision
research groups
·
Vision related
links on AAAI.org page
·
CS
395T Spring 2007 project ideas
·
Draft
chapters from Forsyth and Ponce book
·
Linear
algebra review / primer by Martial Hebert
·
Computer Vision:
Algorithms and Applications, book draft by Richard Szeliski
·
Matlab tutorials and quick references:
Tutorial
code (from Stefan Roth)
Matlab image processing toolbox, getting started
Matlab: Getting Started (guide from Mathworks)