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- Staff
- Web Page
- http://www.cs.washington.edu/education/courses/cse576/09sp/
- Handouts
- signup sheet
- intro slides
- image filtering slides
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- Intros
- Computer vision overview
- Course overview
- Image processing
- Readings
- Book: Richard Szeliski, Computer Vision: Algorithms and
Applications
- (please check Web site weekly for updated drafts)
- Intro: Ch 1.0
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- Goal of computer vision is to write computer programs that can interpret
images
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- Yes and no (but mostly no!)
- humans are much better at “hard” things
- computers can be better at “easy” things
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- The next slides show some examples of what current vision systems can do
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- Many new digital cameras now detect faces
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- This is becoming real:
- Microsoft
Research
- Point & Find, Nokia
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- Mobileye
- Vision systems currently in high-end BMW, GM, Volvo models
- By 2010: 70% of car
manufacturers.
- Video demo
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- You just saw examples of current systems.
- Many of these are less than 5 years old
- This is a very active research area, and rapidly changing
- Many new apps in the next 5 years
- To learn more about vision applications and companies
- David Lowe maintains an excellent overview of vision companies
- http://www.cs.ubc.ca/spider/lowe/vision.html
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- http://www.cs.washington.edu/education/courses/cse576/09sp/
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- http://www.cs.washington.edu/education/courses/cse576/05sp/projects/proj2/artifacts/winners.html
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- TBA
- either an open-ended team “research project”
- or a build-a-vision-system challenge
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- Based on projects
- No midterm or final
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- Prerequisites—these are essential!
- Data structures
- A good working knowledge of C and C++ programming
- (or willingness/time to pick it up quickly!)
- Linear algebra
- Vector calculus
- Course does not assume prior imaging experience
- computer vision, image processing, graphics, etc.
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Notes