1. Computer Vision (CSE 455) Staff Web Page Handouts
Prof: Steve Seitz )
TAs: David Dewey Jiwon Kim
Web Page
Handouts
course info
signup sheet

2. Today Overview of Computer Vision Overview of Course Image Filtering
Readings for this week
Forsyth & Ponce, chapters
Intelligent Scissors

3. Every picture tells a story
Goal of computer vision is to write computer programs that can interpret images

4. Can computers match human perception?
Not yet
computer vision is still no match for human perception
but catching up, particularly in certain areas

5. Perception

6. Perception

7. Perception

8. Low level processing Low level operations
Image enhancement, feature detection, region segmentation

9. Mid level processing Mid level operations
3D shape reconstruction, motion estimation

10. High level processing High level operations
Recognition of people, places, events

11. Application: Document Analysis
                                                
Digit recognition, AT&T labs

12. Applications: 3D Scanning
Scanning Michelangelo’s “The David”
The Digital Michelangelo Project -
UW Prof. Brian Curless, collaborator
2 BILLION polygons, accuracy to .29mm

13. Applications: Motion Capture, Games

14. Application: Medical Imaging

15. Applications: Robotics

16. Project 1: Intelligent Scissors

17. Project 2: Panorama Stitching

18. Project 3: Single View Modeling

19. Project 4: Face Recognition

20. Class Webpage

21. Grading Programming Projects (70%) Midterm (15%) Final (15%)
image scissors
panoramas
single view modeling
face recognition
Midterm (15%)
Final (15%)

22. General Comments Prerequisites—these are essential!
Data structures (CSE 326)
A good working knowledge of C and C++ programming
Linear algebra
Vector calculus
Course does not assume prior imaging experience
computer vision, image processing, graphics, etc.
Emphasis on programming projects!