CSE152
Introduction
to Computer Vision
Lecture:
Tuesday & Thursday
Discussion:
Monday
http://www.cse.ucsd.edu/classes/sp05/cse152/
COURSE ANNOUNCEMENTS
The course time/location has
been moved to Tuesday/Thursday 2:00-3:20 in Sequoyah Hall, Room 148.
Excellent review slides by Tim Marks
on linear algebra and random variables online!
The class webboard has been
created!� It can be accessed here.
The discussion section this week
(April 18) has been moved to Will’s office.� Please stop by if you have questions!
The reading for Binary Images, Horn
Chapters 2 & 3, is here.
Assignment 2 has been posted.� A smaller version of the test movie is also
available.
Solutions to Assignment 1 have been
posted.
Assignment 3 has been posted.
Typos in Assignment 3
(elevation & azimuth for the extra credit problem) have been
corrected.� Please use the new version!
Will is holding extra office hours
on Wednesday May 25 from 1:30pm to 4:30pm.�
Please come if you need help with the photometric stereo assignment.
The last assignment (Assignment 4)
has been posted.
To help you with Assignment 4, you
can read the Eigenfaces vs. Fisherfaces paper here.
The due date for Assignment 4 as
been postponed to Saturday, June 4 at 5pm.
The final exam will be held on
Wednesday, June 8 at 3pm - 6pm in Sequoyah Hall, Room 148.
If you have any
questions before the final, please come to Will’s office hours on Monday,
June 6.
COURSE INFORMATION
Instructor: David Kriegman
Office: AP&M 3101
Phone: (858) 822-2424
Email: [email protected]
Office Hours: Wednesday 1:30-3:00
Location: AP&M 6313
Email: [email protected]
Office Hours: Mon
Class Description: The goal of computer vision is to
compute properties of the three-dimensional world from images and video.
Problems in this field include identifying the 3D shape of a scene, determining
how things are moving, and recognizing familiar people and objects. This
course provides an introduction to computer vision, including such topics as
feature detection, image segmentation, motion estimation, object recognition,
and 3D shape reconstruction through stereo, photometric stereo, and structure
from motion.4 units.
Required Text: Introductory Techniques for 3-D
Computer Vision, E. Trucco and A. Verri, Prentice Hall, 1998.
Prerequisites: Linear algebra and Multivariable
calculus (e.g., Math 20A & 20F), data structure/algorithms (e.g., CSE100),
a good working knowledge of C,C++, or Matlab programming.
Assignments: 45%
Midterm: � 20%
Final Exam: 35%
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
|
Week |
Date/ Link
to lecture notes |
|
|
1 |
Intro to Computer Vision / T&V Chapter 1 |
|
|
|
Human Visual System |
|
|
2 |
Image
Formation, T&V pp.15-19 |
|
|
Color, Color is well-treated in many image-processing texts. Some reasonable on-line sources include: Basics of Color, A FAQ on Color |
||
|
3 |
Segmentation & Binary images, Horn Chapters 2&3, available at e-reserves See on-line resource |
|
|
|
Binary Images &/Filtering |
|
|
4 |
Filtering, T&V pp. 55-63 |
|
|
|
Canny Edge detection, T&V 67-81 |
|
|
5 |
Curves, Hough Transforms, T&V, pp. 97-100 |
|
|
|
Intro to Shape-from-x, Midterm Review |
|
|
6 |
Midterm |
|
|
|
Stereo I, T&V pp. 140-171 |
|
|
7 |
Stereo II |
|
|
|
Photometric Stereo ,T&V pp. 140-171 |
|
|
8 |
Discrete structure from Motion , T&V pp.195-202, 208-211 |
|
|
|
Continuous motion T&V pp. 178-194 |
|
|
9 |
Optical Flow |
|
|
Statistical Pattern Recognition, T&V pp. 248, 262-269 |
||
|
10 |
Appearance-based recognition, “Finding Templates Using Classifiers”, Forsyth & Ponce |
|
|
|
Model-based recognition/Final Exam Review, T&V 249-261 |
2.: Some other books and resrouces.
Computer Vision -- A Modern
Approach, Forsyth and
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)
CV-online: A useful on-line
compendium of Computer Vision sources
�
Camera Calibration
Toolbox for Matlab (Bouguet)
�
Microsoft Camera
Calibration Code (Zhang)
�
The Computer Vision Home
Page
�
Handy Math reference: MathWorld
� Related classes at UCSD:� CSE 166, CSE252A, CSE 252B
