CSE252A
Computer Vision I
Tuesday, Thursday
Center Hall, Rm. 208
http://www.cs.ucsd.edu/classes/wi04/cse291-c/
Class mailing list: cse252a at graphics.ucsd.edu
Class web board: http://www.etalonsoft.com/cse252a/
News:
CSE252A.
Instructor: David Kriegman
Office: AP&M 3101
Phone: (858) 822-2424
Email: kriegman at cs.ucsd.edu
Office Hours: Wednesday
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.
Note that this course will be renamed CSE252A in the
future, and a companion course,
CSE252B, Computer Vision II will be introduced in
the spring quarter.
4
units.
Required Text: “Computer vision: A Modern Approach” David A. Forsyth, Jean Ponce, Prentice Hall, ISBN: 0130851981
Prerequisites: Linear algebra and Multivariable calculus (e.g., Math 20A & 20F), programming, data structure/algorithms (e.g., CSE100)
Programming: 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.
Grading:
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.
Assignments:
Homework 0: Getting Started with Matlab, Due
Tuesday,
Homework 1: Photometric Stereo, Due
Homework 2: Radiometry and Cameras, Due
Homework 3: Stereo, Due
Homework
4: Face Recognition, Due
Syllabus
[ Note that this Syllabus is tentative &
subject to change]
|
Week |
Date/ Link to lecture notes |
Topic/Readings |
|
1 |
Intro to Computer Vision |
|
|
|
Human Visual System, F&P sec. 1.3 |
|
|
2 |
Rigid
Transformatoins SE(3), SO(3), Camera & Lenses, F&P Sec. 2.1, F&P
Chap. 1 |
|
|
|
Perspective, Affine, orthographic projection, F&P 2.2, 2.3 |
|
|
3 |
Radiometry (Irradiance, Radiance, BRDF), F&P Chapter 4 |
|
|
|
Special BRDF’s, Light Sources, Photometric Stereo, F&P, 5.2-5.4 |
|
|
4 |
Photometric stereo |
|
|
|
Illumination Cones, Belhumeur, Kriegman, What Is the Set of Images of an Object under All Possible Illumination Conditions?, IJCV 28(3), 1998, 245-260 |
|
|
5 |
Filtering, F&P Chap. 7, Corner detection |
|
|
|
Edge detection, (Canny) F&P Chap. 8 |
|
|
6 |
Color; F&P, Chap. 6 Some on-line sources include: Basics of Color, A FAQ on Color |
|
|
|
Epipolar Constraint and Stereo I, F&P Sec. 10.1 |
|
|
7 |
Stereo II, Dynamic Programming, Chapter 11 |
|
|
|
Optical Flow, Trucco and Verri , pp. 178-194 |
|
|
8 |
Infinitesimal structure from Motion, Trucco and Verri pp. 195-202, 208-211 |
|
|
|
Tracking, F&P Chap.17 |
|
|
9 |
Statistical Pattern Recognition, F&P 22.1-22.3 |
|
|
|
Support Vector Machines & Kernel Methods, F&P Sec. 22.5, 22.8 |
|
|
10 |
Appearance-based Recognition, Eigenface, Fisherface, Appearance Manifolds |
|
|
|
Model-based recognition, F&P Chap. 18 |
Notes and links
Programming languages: The primary language will be Matlab. . Click here for Serge Belongie’s Matlab resource links.
Another excellent textbook: Introductory Techniques for 3-D Computer Vision Trucco and Verri
CV-oneline: A useful on-line compendium of Computer Vision sources
Handy Math reference: MathWorld
Some other useful vision links
