CS576 Computer Vision
Instructor: Dr. Yu-Wing Tai
No official TA
Tuesday and Thursday
4:00pm – 5:30pm
Rm 3444 E3-1 Building
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Who am I ?
Course Webpage:
http://yuwing.kaist.ac.kr/courses/CS576/index.html
Instructor Info:
Dr. Yu-Wing Tai
yuwing@cs.kaist.ac.kr / yuwing@gmail.com
Education:
PhD National University of Singapore 2009
M.Phil Hong Kong University of Science and Technology 2005
B.Eng Hong Kong University of Science and Technology 2003
Research: Computer Vision, Image/Video Processing
Web: http://yuwing.kaist.ac.kr/
Office hours: arrange by email.
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Grading (Absolute Grading)
• You start with the letter grade “C-”, you earn
sub-grade from:
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–
–
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2 course projects (2 sub-grades)
1 term project (1-2 sub-grades)
Peer review of term project (1 sub-grade)
1 oral examination (1 sub-grade)
Bonus (See project description)
Attendant (At least 60%, 1 sub-grade)
• Best Grade: A+ (No limit on number of A+)
• Worst Grade: D+ (No exception for better grade)
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Project 1: Feature Detection and Matching
• Descriptions:
– http://yuwing.kaist.ac.kr/courses/CS576/project1/index.html
• Deadline: Friday mid-night (00:00) on Week 5
• A project directly copy from the first project of the
computer vision class in U. Washington
• 1 sub-grade for this project; 0.5 sub-grade for
unsuccessful implementation; 0 sub-grade for late
submission
• No bonus sub-grade for Project 1
• Goal:
– Fundamental project for computer vision class
– Learning how to use skeleton codes from others
– Learning how to find related internet resources for project
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Project 2: Paper implementation
• Descriptions:
– http://yuwing.kaist.ac.kr/courses/CS576/project2/index.html
• Deadline: Friday mid-night (00:00) on Week 9
• 1 sub-grade for this project; 0.5 sub-grade for
unsuccessful implementation; 0 sub-grade for late
submission
• Maximum 1 Bonus sub-grade for extra successful
submission
• Goal:
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Paper reading
Find your own interests area
Learning how to find related resources
Learning how to reproduce previous research projects
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Term Project: A mini-conference submission
• Descriptions:
– http://yuwing.kaist.ac.kr/courses/CS576/termproject/index.html
• Deadline: Friday mid-night (00:00) on Week 13
• 1 sub-grade for submission; 1 sub-grade for acceptance;
0 sub-grade for late submission
• Acceptance rate will be about 30% - 50%
• 1 sub-grade for peer evaluation
• Bonus sub-grade for best presentation
• Bonus sub-grade for 2 outstanding reviewers
• Bonus sub-grade(s) for extra-acceptance project
• Goal:
– Understand the process of research cycle and paper submission
– Testing your abilities/potentials in doing research/getting PhD
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Oral Exam
• 10-15 minutes face-to-face question and
answer session testing your knowledge
and your understanding to the term project
• Week 15
• 1 sub-grade for passing; 0.5 sub-grade for
failure; -1 sub-grade for absent
• Goal:
– Testing your knowledge
– Getting course feedback
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Course Resources
• Books (Available for borrowing):
– Computer Vision: Algorithms and Applications © 2010, Richard Szeliski
– Computer Vision: A Modern Approach © 2002, David A. Forsyth and
Jean Ponce
• Computer Vision papers:
http://www.gmazars.info/conf/
• Computer Graphics papers:
http://kesen.huang.googlepages.com/
• Microsoft Academic Search
http://academic.research.microsoft.com/
• Google
http://www.google.com/
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Course Resources
• Computer Vision Source Codes
http://www.cs.cmu.edu/~cil/v-source.html
• OpenCV
http://sourceforge.net/projects/opencvlibrary/
• The Middlebury Computer Vision Pages
http://vision.middlebury.edu/
• Computer Vision Algorithm Implementations
http://www.cvpapers.com/rr.html
• Computer Vision Datasets
http://clickdamage.com/sourcecode/cv_datasets.html
• Columbia University Computer Vision Lab
http://www.cs.columbia.edu/CAVE/
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Other Texts
• UNDERGRADUATE
– A Guided Tour of Computer Vision, by V. S. Nalwa, AddisonWesley, 1993.
– Introductory Techniques for 3-D Computer Vision, by Emanuele
Trucco, Alessandro Verri, Prentice-Hall, 1998
• GRADUATE/ Specialized Reference
– Multiple View Geometry, by Richard Hartley, Andrew Zisserman,
Cambridge University Press, 2000.
– Numerical Recipes in C, by William Press et al., Cambridge Univ
Press, 1992.
– Pattern Classification and Scene Analysis, by Richard O. Duda,
Peter E. Hart, John Wiley & Sons, 1973.
– Convex Optimization, by Stephen Boyd and Lieven
Vandenberghe, 2010.
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What is Computer Vision ?
• In 1966, Marvin Minsky at MIT asked his undergraduate student
Gerald Jay Sussman to “spend the summer linking a camera to a
computer and getting the computer to describe what it saw”
• “In the 1960s, almost no one realized that machine vision was
difficult.” – David Marr, 1982
• 40+ years later, we are still working on this…
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What is Computer Vision ?
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The goal of computer vision
• To bridge the gap between pixels and “meaning”
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What is Computer Vision ?
From Wikipedia
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What is Computer Vision ?
Computer
Vision
Image
Processing
Image Enhancement,
Denoising, etc.
Stereo,
3D Reconstruction, etc.
Computer
Graphics
Rendering,
3D Modelling, etc.
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1970s
line labeling
intrinsic images
pictorial structures
stereo correspondence
articulated body model
optical flow
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1980s
pyramid blending
physically-based
models
shape from shading
edge detection
regularization-based range data acquisition
surface reconstruction
and merging
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1990s
factorization-based
structure from motion
face tracking
dense stereo matching
image segmentation
multi-view
reconstruction
face recognition
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2000s
image-based rendering image-based modeling
texture synthesis
Interactive Techniques
feature-based recognition region-based recognition
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State-of-the-art ?
http://www.xbox.com/en-US/kinect
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Why study computer vision ?
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Why study computer vision ?
• Vision is useful
• Vision is interesting
• Vision is difficult
– Half of primate cerebral cortex is devoted to
visual processing
– Achieving human‐level visual perception is
probably “AI‐complete”
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Challenges: viewpoint variation
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Challenges: Illumination
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Challenges: Scale
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Challenges: Deformation
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Challenges: Occlusion
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Challenges: Background cluster
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Challenges: Motion
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Challenges: Object intra-class variation
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Challenges: Local Ambiguities
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Challenges or opportunities?
• Images are confusing, but they also reveal
the structure of the world through
numerous cues
• Our job is to interpret the cues!
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Computer Vision in the Real World
• Special Effects in movie
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Computer Vision in the Real World
• 3D Urban Modeling
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Computer Vision in the Real World
• Microsoft Photosynth (http://labs.live.com/photosynth/)
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Computer Vision in the Real World
• Face Detection
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Computer Vision in the Real World
• Biometrics
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Computer Vision in the Real World
• Optical Character Recognition (OCR)
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Computer Vision in the Real World
• Toys and Robots
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Computer Vision in the Real World
• Games
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Computer Vision in the Real World
• Automotive Safety
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Computer Vision in the Real World
• Vision for robotics, space exploration
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Goal of this course
• Broaden your view about computer vision,
but we are not going to study any specific
topic in deep
• Teaching you how to find computer vision
research resources yourself
• Understand the research world and
teaching you how to be a good computer
vision scientist
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Question ?
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