1. Lecture 1: Introduction
CAP5416 Computer Vision
Lecture 1: Introduction

2. What is computer vision?
An Example :
Clips: terminator 2, enemy of the state (from UCSD “Fact or Fiction” DVD)
Terminator 2

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

4. Can computers match (or beat) human vision?
If you can write a formula for it, computers can excel
Computer vision can’t solve the whole problem (yet), so breaks it down into pieces.
Many of the pieces have important applications.
Yes and no (but mostly no!)
humans are much better at “hard” things
computers can be better at “easy” things

5. Optical illusions
Copyright A.Kitaoka 2003

6. Face recognition
Who is she?

7. Vision-based biometrics
“How the Afghan Girl was Identified by Her Iris Patterns” Read the story

8. Login without a password…
Face recognition systems now beginning to appear more widely
Fingerprint scanners on many new laptops, other devices

9. Optical character recognition
Technology to convert scanned docs to text
If you have a scanner, it probably came with OCR software
Digit recognition, AT&T labs
License plate readers

10. Earth viewers (3D modeling)
Image from Microsoft’s Virtual Earth
(see also: Google Earth)

11. Google streetview

12. by Noah Snavely, Steve Seitz, and Rick Szeliski
Photosynth
by Noah Snavely, Steve Seitz, and Rick Szeliski

13. Face detection Many new digital cameras now detect faces
Why would this be useful? Main reason is focus. Also enables “smart” cropping.
Many new digital cameras now detect faces
Canon, Sony, Fuji, …

14. Smile detection?
Sony Cyber-shot® T70 Digital Still Camera

15. Object recognition (in supermarkets)
LaneHawk by EvolutionRobotics
“A smart camera is flush-mounted in the checkout lane, continuously watching for items. When an item is detected and recognized, the cashier verifies the quantity of items that were found under the basket, and continues to close the transaction. The item can remain under the basket, and with LaneHawk,you are assured to get paid for it… “

16. Object recognition (in mobile phones)
This is becoming real:
Microsoft Research
Point & Find, Nokia

17. Special effects: shape capture
The Matrix movies, ESC Entertainment, XYZRGB, NRC

18. Special effects: motion capture
Pirates of the Carribean, Industrial Light and Magic
Click here for interactive demo

19. Sports Sportvision first down line
Nice explanation on

20. Slide content courtesy of Amnon Shashua
Smart cars
Slide content courtesy of Amnon Shashua
Mobileye
Vision systems currently in high-end BMW, GM, Volvo models
By 2010: 70% of car manufacturers.

21. Vision-based interaction (and games)
Digimask: put your face on a 3D avatar.
Nintendo Wii has camera-based IR tracking built in. See Lee’s work at CMU on clever tricks on using it to create a multi-touch display!
“Game turns moviegoers into Human Joysticks”, CNET
Camera tracking a crowd, based on this work.

22. Vision-based HCI
Reatrix:

23. Gaming
Sony Eyetoy
Microsoft Natal

24. Looking at people
Hand gesture
Head pose
Expression
Identity

25. Vision in space Vision systems (JPL) used for several tasks
NASA'S Mars Exploration Rover Spirit captured this westward view from atop a low plateau where Spirit spent the closing months of 2007.
Vision systems (JPL) used for several tasks
Panorama stitching
3D terrain modeling
Obstacle detection, position tracking
For more, read “Computer Vision on Mars” by Matthies et al.

26. Gigapan http://www.gigapan.org/index.php
HP TouchSmart with Gigapn demo at Chicago O’Hare airport

27. NASA’s Mars Spirit Rover
Robotics
NASA’s Mars Spirit Rover

28. Medical imaging Image guided surgery 3D imaging Grimson et al., MIT
MRI, CT

29. Digital comestics

30. Inpainting
Bertalmio et al. SIGGRAPH 00 30

31. Debluring
Fergus et al. SIGGRAPH 06 31

32. Image and video search
Google
YouTubes
Microsoft
Yahoo 32

33. Current state of the art
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

34. Related topics

35. How does one do this? Camera …. Compute features
Recognition (Match/register features)

36. Basic Topics: Camera Image Processing Mid-Level Vision
Studying Camera Models
How to infer the geometry of the observed scene
Image Processing
Filtering (Image Smoothing)
Edge Detection (Computing Features)
Mid-Level Vision
Segmentation
Registration.
High-Level Vision – recognition/detection
Statistical Analysis, Mathematical Analysis with goal of interpreting images.

37. (Possible) Project 1: Feature Computation

38. Project 2: Panorama stitching
Stitching Images into a single panoramic image
Indri Atmosukarto, sp

39. Project 3: Object detection