Computer Vision (CSE 455)
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Staff |
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Web Page |
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http://www.cs.washington.edu/education/courses/cse455/08wi/ |
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Handouts |
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signup sheet |
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intro slides |
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image filtering slides |
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Today
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Intros |
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Computer vision overview |
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Course overview |
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Image processing |
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Readings for this week |
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Forsyth & Ponce, chapter 7
(in reader, available at UW Bookstore in the CSE textbook area) |
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Mortensen, Intelligent Scissors
(online) |
What is computer vision?
What is computer vision?
Every picture tells a
story
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Goal of computer vision is to
write computer programs that can interpret images |
Can computers match (or
beat) human vision?
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Yes and no (but mostly no!) |
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humans are much better at
“hard” things |
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computers can be better at
“easy” things |
Human perception has its
shortcomings…
Slide 8
Current state of the art
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The next slides show some
examples of what current vision systems can do |
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Earth viewers (3D
modeling)
Photosynth
Optical character
recognition (OCR)
Face detection
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Many new digital cameras now
detect faces |
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Canon, Sony, Fuji, … |
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Smile detection?
Object recognition (in
supermarkets)
Face recognition
Vision-based biometrics
Login without a password…
Object recognition (in
mobile phones)
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This is becoming real: |
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Microsoft Research |
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Point & Find, Nokia |
Special effects: shape capture
Special effects: motion capture
Sports
Smart cars
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Mobileye |
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Vision systems currently in
high-end BMW, GM, Volvo models |
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By 2010: 70% of car manufacturers. |
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Video demo |
Vision-based interaction
(and games)
Vision in space
Robotics
Medical imaging
Current state of the art
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You just saw examples of
current systems. |
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Many of these are less than 5
years old |
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This is a very active research
area, and rapidly changing |
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Many new apps in the next 5
years |
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To learn more about vision
applications and companies |
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David Lowe maintains an
excellent overview of vision companies |
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http://www.cs.ubc.ca/spider/lowe/vision.html |
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This course
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http://www.cs.washington.edu/education/courses/cse455/08wi/ |
Project 1: intelligent scissors
Project 2: panorama stitching
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http://www.cs.washington.edu/education/courses/455/06wi/projects/project2/results.html |
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Project 3: 3D shape reconstruction
Project 4: Face Recognition
Grading
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Programming Projects (70%) |
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image scissors |
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panoramas |
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3D shape modeling |
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face recognition |
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Midterm (15%) |
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Final (15%) |
General Comments
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Prerequisites—these are
essential! |
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Data structures |
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A good working knowledge of C
and C++ programming |
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(or willingness/time to pick it
up quickly!) |
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Linear algebra |
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Vector calculus |
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Course does not assume prior
imaging experience |
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computer vision, image
processing, graphics, etc. |
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