CS 376: Computer Vision
Spring 2011

Mon/Wed 11:00 am - 12:15 pm
UTC 3.124

Instructor: Kristen Grauman
Office location: ACE 3.446
Office hours: Wed 5-6 pm, and by appointment.

TA: Shalini Sahoo shalini@cs.utexas.edu
Office location: PAI 5.33 TA station, desk 3
Office hours: Tues/Thurs 5-6 pm

TA (office hours only): Yong Jae Lee
Office location: PAI 5.33 TA station, desk 3
Office hours: Mon 5-6 pm
Please come to any of our office hours for questions about assignments or lectures.

Questions via email about an assignment should be sent to:
cv-spring2011@cs.utexas.edu, with "CS376" in the beginning of the subject line.
This will ensure the most timely response from the instructor or TA.


The final exam slot has been confirmed by the registrar: Monday May 16, 2-5 pm, in JGB 2.102.  You may bring two sheets of notes on 8.5 x 11" paper.  The exam is comprehensive.

View all current grades and late days used on Blackboard.


Course description: 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?

In computer vision, 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 video processing.  This course is intended for upper-level undergraduate students. 

Textbook: The
textbook is Computer Vision: Algorithms and Applications, by Rick Szeliski.  It is currently available for purchase e.g. at Amazon for ~$65.  An electronic copy is also available free online here.  I will also select some background reading on object recognition from this short book on Visual Object Recognition that I prepared together with Bastian Leibe.

Syllabus: Details on prerequisites, course requirements, textbooks, and grading policy are posted
here.    A high-level summary of the syllabus is here.

Problem set deadlines: Assignments are due about every two weeks.  The dates below are tentative and are provided to help your planning.  They are subject to minor shifts if the lecture plan needs to be adjusted slightly according to our pace in class. 


Readings and links
Assignments, exams

Wed Jan 19
Course intro
Sec 1.1-1.3

Pset 0 out Friday Jan 21
Mon Jan 24
Features and filters
Sec 3.1.1-2, 3.2
Linear filters
[ppt] [pdf] [outline]

Wed Jan 26

Sec 3.2.3, 4.2

Seam carving paper
Seam carving video
Gradients and edges
[ppt] [pdf] [outline]
Pset 0 due Friday Jan 28
Mon Jan 31

Sec 3.3.2-4
Binary image analysis
[ppt] [pdf] [outline]
Pset 1 out [class results]
Wed Feb 2

Sec 10.5

Texture Synthesis
[ppt] [pdf] [outline]

Mon Feb 7
Sec 2.3.2

Foundations of Color, B. Wandell

Lotto Lab illusions
[ppt] [pdf] [outline]
Pset 0 grades and solutions returned in class

Wed Feb 9
Grouping and fitting Sec 5.2-5.4

k-means demo

Segmentation and clustering
[ppt] [pdf] [outline]

Mon Feb 14

Sec 4.3.2

Hough Transform demo

Excerpt from Ballard & Brown

Hough transform
[ppt] [pdf] [outline]

Pset 1 due Monday Feb 14
Pset 2 out
Wed Feb 16
Mon Feb 21

Sec 5.1.1
Deformable contours
[ppt] [pdf] [outline]

Wed Feb 23

Sec 2.1.1, 2.1.2, 6.1.1
Alignment and 2d image transformations
[ppt] [pdf] [outline]
Pset 1 grades and solutions returned in class

Mon Feb 28
Multiple views and motion
Sec 3.6.1, 6.1.4
Homography and image warping
[ppt] [pdf] [outline]

Wed Mar 2

Sec 4.1
Local invariant features 1
[ppt] [pdf] [outline]
Pset 2 due Wednesday Mar 2
Mon Mar 7

(Sec 4.1) Local invariant features 2
[ppt] [pdf] [outline]

Wed Mar 9

Midterm exam

Pset 2 grades and solutions returned in class
Spring break

Pset 3 out  [class results]
Mon Mar 21

Sec 11.1.1, 11.2-11.5
Image formation (and local feature matching wrap-up)
[ppt] [pdf] [outline]

Wed Mar 23

Sec 11.1.1, 11.2-11.5

Epipolar geometry demo

Audio camera, O'Donovan et al.
Stereo 1: Epipolar geometry
[ppt] [pdf] [outline]

Mon Mar 28
Virtual viewpoint video, Zitnick et al.
Stereo 2: Correspondence and calibration
[ppt] [pdf] [outline]

Wed Mar 30
Recognition Grauman & Leibe Ch 1-4 (3 is review)

Indexing local features
[ppt] [pdf] [outline]
Pset 3 due Wed March 30
Mon April 4

Grauman & Leibe Ch 5, 6

Szeliski 14.3

Video Google demo by Sivic et al., paper
Instance recognition
[ppt] [pdf] [outline]

Wed April 6

Grauman & Leibe Ch 7, 8.1, 9.1, 11.1

Szeliski 14.1
Intro to category recognition
[ppt] [pdf] [outline]
Pset 3 grades and solutions returned in class
Pset 4 out
Mon April 11

Grauman & Leibe Ch 7, 8.1, 9.1, 11.1

Szeliski 14.1

Viola-Jones face detection paper (for additional reference)
Face detection
[ppt] [pdf] [outline]

Wed April 13

Grauman & Leibe
11.3, 11.4

Szeliski 14.4
Discriminative classifiers for image recognition
[ppt] [pdf] [outline]

Mon April 18

Grauman & Leibe
11.3, 11.4

Szeliski 14.4
Part-based models
[ppt] [pdf] [outline]

Wed April 20
Video processing
8.4, 12.6.4
[ppt] [pdf] [outline]

Pset 4 due Wed April 20
Mon April 25

8.4, 12.6.4

Davis & Bobick paper: The Representation and Recognition of Action Using Temporal Templates

Stauffer & Grimson paper: Adaptive Background Mixture Models for Real-Time Tracking.

Background subtraction, Action recognition
[ppt] [pdf] [outline]

Pset 5 out
Wed April 27

5.1.2, 4.1.4
[ppt] [pdf]

Pset 4 grades and solutions returned
Mon May 2

Course wrap-up and review

Wed May 4

Pset 5 due Sun May 8
Mon May 16
2-5 pm

Final exam in JGB 2.102