EE 606 Advanced Computer Vision
Syllabus
Spring 2008-2009
Sabancı University
Office Number & Hours: FENS G045
Telephone: 9553
Instructor Name: Gozde Unal
Email: gozdeunal@sabanciuniv.edu
by appointment via email
Course web site: SUCourse
Lecture:
Hours: Monday 14:40 – 17:30
Location: FENS G029
Credits: 3
Course Description
The aim of the course is to study computer vision, which tries to “make computers see and interpret” using the
observations in the form of multiple 2D images or 3D images. Sophisticated computational techniques are
developed with the goal of estimating and making inferences about the geometric and dynamic properties of the
3D world around us.
A tentative list of topics for the course includes: Linear algebra review, Groups, Rotation Group, Image
formation, Camera models, Camera calibration, Feature extraction and matching, RANSAC, introductory
projective geometry, homography estimation, Calibrated and Uncalibrated Epipolar geometry, 3D
reconstruction from two views, Volumetric 3D reconstruction approaches including multiple view
reconstruction, motion/dynamic vision, Introductory Differential Geometry of Surfaces, 3D Segmentation
topics: Model-based 3D Segmentation, shape priors, Registration related Topics,…
The course will provide the participants with an up to date background in Advanced Computer Vision topics,
mainly in 3D vision.
Prerequisites
Math background (linear algebra), Programming background (one programming language like Matlab, C/C++
(Visual Studio), QT, VTK), 2D and/or 3D image processing. Most importantly enthusiasm to learn this popular
and fun subject: A prior course in computer graphics, computer vision, image processing is recommended.
Students lacking these requirements should speak with the instructor for obtaining permission to enroll.
References
*
Reference books:
1. Introductory Techniques for 3-D Computer Vision, E. Trucco, A. Verri, Prentice Hall 1998.
@RESERVE COLLECTION at SU Information Center.
2. An Invitation to 3D Vision, Ma, Soatto, Kosecka, Sastry, Springer, 2006. @RESERVE
COLLECTION at SU Information Center.
3. Multiple View Geometry in Computer Vision, R. Hartley and A. Zisserman, Cambridge University
Press, 2000, or 2nd edition 2003, Electronic Book Accessible at SU Information Center.
4. D. Forsyth and J. Ponce, Computer Vision: A modern approach, 2004, Prentice Hall.
* Related Conferences: IEEE CVPR (Computer Vision and Pattern Recognition Conference), IEEE ICCV
(Int. Conf. Computer Vision), ECCV (European Conference on Computer Vision), 3DIM (Int. Conf. on 3-D
Digital Imaging and Modeling), 3DPVT (3-D Data Processing, Visualization and Transmission),…
* Related Journals: IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), International
Journal of Computer Vision, Journal of Mathematical Imaging and Vision, Computer Aided Design,…
Grading (Tentative Evaluation)
Homework Assignments
35%
Class Participation
5%
Midterm (=Final) Exam
25%
Course Project Write-up and Innovation
15%
Project Implementation
10%
Course Project Presentation and Demo
10%
Course Project Timeline (deadlines):
March 16: Decide your topic (or the paper on which you want to build and improve)
April 6: First progress report with literature review and a clear Outline of your paper and method
May 11: Second progress report with method and preliminary results (should be roughly getting closer to
its final form)
End of May: Final project submission and presentation
Tentative Course Schedule
Week
1
Date
Feb 16
Topic
Introduction to Computer Vision
Image Formation
Camera Models
Linear Algebra Review
Rigid-body Motion
Projective Plane: introduction
2
3
Feb 23
Mar 2
4
5
Mar 9
Mar 16
No class
Projective Geometry: more intro
Camera Calibration
6
7
Mar 23
Mar 30
Homography Estimation
Calibrated Epipolar Geometry
3D Reconstruction
8
Apr 6
9
Apr 13
10
11
Apr 20
Apr 27
12
13
May 4
May 11
14
May 18
15
May 25
Uncalibrated Epipolar Geometry
3D Reconstruction
3D Reconstruction Wrapup/Volumetric approaches
Spring Break
Diff Geom. of Curves and
Surfaces
EXAM
Recent developments in using
variational methods and PDE's to
represent and recover surfaces,
Implicit Shape Representations
(level sets), Model-based
Segmentation, shape priors, …
Graph cuts etc
Motion/Dynamic Images
Shape from X
Registration, stitching, morphing
Reading
[TruccoVerri] 2.2,
2.4
Assignment/Notes
[MaSoatto] Chap 2
[Hartley] Chap 3
Unal @ SIAM conference
[TruccoVerri]
Chap 6, Zhang’s
Cam. Calibration
No Class
Each week: Monday Block Course
Note: This syllabus and schedule are subject to change. If you are absent from class, it is your responsibility to
check on announcements made while you were absent.
Course Policies
Discussions among students for course assignments and projects are encouraged. However, what you submit
should be output of your own efforts and work.
Cheating and Plagiarism: "Cheating is the actual or attempted practice of fraudulent or deceptive acts for the
purpose of improving one's grade or obtaining course credit; such acts also include assisting another student to
do so. Typically, such acts occur in relation to examinations. However, it is the intent of this definition that the
term 'cheating' not be limited to examination situations only, but that it include any and all actions by a student
that are intended to gain an unearned academic advantage by fraudulent or deceptive means. Plagiarism is a
specific form of cheating which consists of the misuse of the published and/or unpublished works of others by
misrepresenting the material (i.e., their intellectual property) so used as one's own work." Penalties for cheating
and plagiarism range from a 0 or F on a particular assignment, through an F for the course, to expulsion from
the university. You can find some related links at the SU web page Ethics in Science:
http://www.sabanciuniv.edu/mdbf/eng/OgretimUyeleri/BilimselAhlakKurallari.html
Disruptive Classroom Behavior: "The classroom is a special environment in which students and faculty come
together to promote learning and growth. It is essential to this learning environment that respect for the rights of
others seeking to learn, respect for the professionalism of the instructor, and the general goals of academic
freedom are maintained. … Differences of viewpoint or concerns should be expressed in terms which are
supportive of the learning process, creating an environment in which students and faculty may learn to reason
with clarity and compassion, to share of themselves without losing their identities, and to develop an
understanding of the community in which they live. … Student conduct which disrupts the learning process
shall not be tolerated and may lead to disciplinary action and/or removal from class."