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CSE 152: Introduction to Computer Vision
Winter 2014
Course Information / Course Outline / Assignments / Notes and Links
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Recent
announcements
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(01/07/2014) This website is online
Course
Information
- Lecture: Tuesday & Thursday 12:30 - 1:50 PM in EBU3b 2154
- Final: March 18, 11.30 AM - 2.29 PM in: TBA
- Discussion: Monday 3 - 3.50 PM in WLH
21132207 (Optional) - Instructor:
Prof. David Kriegman
Office: EBU-3B, Room 4120
Phone: 858-822-2424
Email: kriegman [AT] cs.ucsd.edu
Office Hours: Wed. 2:00 - 3:00 pm - TA:
Oscar Beijbom
Email: obeijbom [AT] cs.ucsd.edu
Office Hours: Thurs. 2.00-3.00pm in B250A.
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.
Required Text: Introductory
Techniques for 3-D Computer Vision, E. Trucco and A. Verri, Prentice
Hall, 1998.
Since the text book is out of print, and the
price of used copies have become outrageous, I've been able to place some of
the reading from Trucco and Verri
on Ereserves at http://reserves.ucsd.edu/eres/coursepage.aspx?cid=21570&page=docs
. The password for this site is on Piazza, under the 'Course Info' tab. Also hardcopies of the book will be available on 3-hour reserve. Optional Text:
Computer Vision: Algorithms and Applications, Richard Szeliski.
An older draft of the book, is available at
http://szeliski.org/Book/ Assignment 0 [due 01/14] Assignment 1 (due Assignment 2 (due 2/18): Here is a PDF file for Assignment 2. Assignment 3 (due 3/7): Here is a PDF file for Assignment 3. Assignment 4 (due 3/14): Here is a PDF file for Assignment 4. **
Tentative syllabus - subject to change. January 7 -
Introduction to Computer Vision, Linear Algebra Intro [T&V Chapter 1, Sz. 1-28] January 9 -
Human Visual System, Cues and Illusions January 14 -
Image Formation, Cameras, Projections and Rigid-body Transformations
[T&V
Chapter 2, pp. 26-40, Szeliski. pp. 29-58] January 16 -
Rigid-body Transformations, Camera Calibration and Lenses [Szeliski pp.7-90] January 21 -
Radiometry (Lighting and Shading) [T&V Chapter 2, pp. 16-26, Szeliski pp.60-72] January 23 -
Color [Gonzalez
and Woods: Chapter 6, Basics
of Color, An
FAQ on Color] January 28 -
Binary Image Processing [ Link
to Chapter 4 of "Robot Vision" by Horn (e-reserves) ] January 30 -
Filtering [T&V,
Chapter 3, pages 55-63, Szeliski pp. 111-121] February 4 -
Edge Detection [T&V,
pages 67-81, Szeliski pp. 238-249] February 6 -
Hough Transform [T&V,
pp. 97-100, Szeliski pp. 250-257] February 11 -
Generalized Hough Transform + Line Fitting - February 13: Midterm (Sample
midterm questions) February 18 -
Stereo (Introduction) [T&V, pp. 140-171, Szeliski pp. 533-558] February 20 -
Stereo (Epipolar Geometry) February 25 -
Stereo (Window-based methods + Dynamic Programming + Helmholtz) February 27 -
Photometric Stereo [Forsyth and Ponce, Chapter 5, pages 80-86, Szeliski
pp. 580-582 ] March 4 -
Photometric Stereo / Introduction to Structure-From-Motion (SFM)
[T&V, pages
191-198, Szeliski pp. 343-368 ] March 6 -
Recognition (PCA, Eigenfaces and Fisherfaces)
[Szeiliski pp. 655-693, Fisherfaces paper] March 11 -
Recognition, Bayesian Classifiers [T&V, pages 262-270] March 13 -
Appearance-Based Recognition, Motion Field & Optical Flow
[T&V, pages
191-198, Szeliski pp. 381-415] - March 18: Final Exam. Details under "Course Information" above. á Programming
languages: For this course, all programming will be in Matlab.
A short tutorial assignment will provide the introduction. Click here for Serge Belongie's Matlab resource links.
á Some other textbooks
and resources á Some useful links
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.
Grading: There will be homework
assignments, a midterm, and a final weighted with the following percentages:
Assignments:
55%
Midterm:
15%
Final
Exam: 30%
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
Test image for assignment: flag image
01/31 2/3): Here is a PDF file for Assignment 1.
Images and data are in this folder.
Images and data are in this folder.
Images and data are in this folder.
Images and data are in this folder.
Course
Outline
Notes
and Links
