Computer Vision I

Tuesday, Thursday 9:30 am - 10:50 am
Pepper Canyon Hall (PCYNH), Room 120


Class web board: http://piazza.com/ucsd/fall2011/cse252a

Class web page: http://cseweb.ucsd.edu/classes/fa11/cse252A-a



11/22: Assignment #4 is posted and is due Saturday, December 3rd.

11/11: Assignment #3 is posted and is due Tuesday, November 22nd.

11/02: Assignment #2 has been extended the new deadline is Tuesday, November 8th.

10/29: Scores for Assignment #1 have been posted on GradeSource.

10/20: Assignment #2 is posted and is due Thursday, November 3rd.

10/12: Scores for Assignment #0 have been posted on GradeSource.

10/08: Figures 3 and 5 of Assignment #1 have been updated.

10/07: Assignment #1 is posted and is due Tuesday, October 18th.

10/06: The room has been changed to Pepper Canyon Hall (PCYNH), Room 120

09/25: Office hours for Prof. Kriegman and the TA have been set. See below for details.

09/22: The first day of class.


Course Information:

Instructor: David Kriegman
Office: EBU3B, Room 4120
Phone: (858) 822-2424
Email: kriegman at cs.ucsd.edu
Office Hours: Tue 1:00 - 2:00 pm


TA: Andrew Ziegler

Email: aziegler at cs.ucsd.edu

Office: EBU3B, Room B260A

Office Hours: Wed 11:00am - 12:50pm

Class Description:
Comprehensive introduction to computer vision providing broad coverage including low level vision (image formation, photometry, color, image feature detection), inferring 3D properties from images (shape-from-shading, stereo vision, motion interpretation) and object recognition. A companion course, CSE252B, Computer Vision II is taught in the Winter quarter. 4 units.

Required Text:
“Computer Vision: Algorithms and Applications”, Richard Szeliski, An online copy of the book, is available at: http://szeliski.org/Book/.

Supplemental Text:
"Computer vision: A Modern Approach," David A. Forsyth, Jean Ponce, Prentice Hall, ISBN: 0130851981

Linear algebra and Multivariable calculus (e.g., Math 20A & 20F), programming, data structure/algorithms (e.g., CSE100). Probability can also be useful.

Assignments will include both written problem sets and programming assignments in Matlab. Students can either purchase the Matlab student edition or use copies available on University machines such as are available in the APE Lab.

Assignments: 60%
Final Exam: 40%

Late Policy:
Written homework will be due in class and accepted thereafter with a penalty of 10% per day starting from the due date. Programming assignments will have a hand-in procedure described with the assignment, and also has a 10% per day late penalty. No assignments will be accepted after the graded assignments have been returned or the solutions have been released.



Homework 0 [Due 10/04]: Getting Started with Matlab

Homework 1 [Due 10/18]: Camera & Lens, Rigid Transformations and Homography

Homework 2 [Due 11/03]: Radiometry and Photometric Stereo

Homework 3 [Due 11/22]: Epipolar Geometry and Sparse Stereo Matching

Homework 4 [Due 12/03]: Optical Flow and Video Stabilization





[ Note that this Syllabus is tentative & subject to change.  Readings denoted F&P are from Computer vision: A Modern Approach “and those denoted by RS are from Computer Vision: Algorithms and Applications.” ]


Date / Link to lecture notes

Topic / Readings


Sep. 22
Linear algebra review
Random variables review

Intro to Computer Vision


Sep. 27

Human Visual System, F&P sec. 1.3. RS 1-18

Sep. 29

Rigid Transformatoins SE(3), SO(3), Camera & Lenses, F&P Sec. 2.1, F&P Chap. 1, RS. 29-45


Oct. 4

Perspective, Affine, orthographic projection, F&P 2.2, 2.3, RS. 46-59

Oct. 6

Radiometry (Irradiance, Radiance, BRDF), F&P Chapter 4; Supplemental reading Glassner Chapter 13 (requires campus network connection)


Oct. 11

Special BRDF's, Light Sources, Photometric Stereo, F&P, 5.2-5.4;  RS, 60-72, Supplemental reading Glassner pp 726-752

Oct. 13

Irradiance example problem

Photometric stereo RS 580-583


Oct. 18

Illumination Cones, Belhumeur, Kriegman, What Is the Set of Images of an Object under All Possible Illumination Conditions?, IJCV 28(3), 1998, 245-260

Oct. 20

Color; F&P, Chap. 6, RS 8-89, Some on-line sources include: Basics of Color, A FAQ on Color


Oct. 25

Color, Dichromatic model, RS 67

Oct. 27

Filtering F&P Chap. 7, 8, RS.  101-1.22


Nov. 1

Edges RS 238=249

Nov. 3

Epipolar Constraint and Stereo I, F&P Sec. 10.1, RS 530-544


Nov. 8

Stereo II, Dynamic Programming, Chapter 11, 545-548, 552-556

Nov. 10

Optical Flow, Trucco and Verri, pp. 178-194, RS 4381-414


Nov. 15

Infinitesimal structure from Motion, Trucco and Verri pp. 195-202, 208-211

Nov. 17

Tracking, F&P Chap.17, RS 235-237, 282-284, 551-552, 605-609


Nov. 22

Statistical Pattern Recognition, F&P 22.1-22.3


Nov. 29

Support Vector Machines & Kernel Methods, F&P Sec. 22.5, 22.8

Dec. 1

Appearance-based Recognition and  Model-based recognition, F&P Chap. 18, RS 655-722

Notes and Links:

Programming languages:
The primary language will be Matlab. . Click here for Serge Belongie's Matlab resource links.

Other excellent textbooks:
Introductory Techniques for 3-D Computer Vision, Trucco and Verri (textbook for CSE152)
An Invitation to 3D Vision: From Images to Geometric Models, Ma, Soatto, Kosecka and Sastry, Springer Verlag, 2003, ISBN 0-387-00893-4 (textbook for CSE252B)

Some useful links:
Camera Calibration Toolbox for Matlab (Bouguet)
Microsoft Camera Calibration Code (Zhang)
Intel OpenCV
The Computer Vision Home Page
Handy Math reference: MathWorld

Related classes at UCSD: CSE 190-A, CSE 166, CSE 252B