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.


Announcements

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.


Overview

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. 

Schedule


 Dates
 Topic
 Readings and links
 Lectures
 Assignments, exams

Wed Jan 19
 Course intro
 Sec 1.1-1.3
 Intro
[pdf]

Pset 0 out Friday Jan 21
filters.jpg
 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
Texture
[ppt] [pdf] [outline]
Mon Feb 7
Sec 2.3.2

Foundations of Color, B. Wandell

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

grouping.jpg
 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

multiview.jpg
 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]

recognition.jpg
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]

video.jpg
 Wed April 20
 Video processing
 8.4, 12.6.4
 Motion
[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
 Tracking
[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



Links