1. EECS 274 Computer Vision Introduction

2. What is computer vision? 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? 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 2003A.Kitaoka

6. Why is computer vision difficult? Inverse problem Ill-posed High-dimensional data Noise Variation

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

8. Google streetview

9. Photosynth http://labs.live.com/photosynth/ http://www.youtube.com/watch?v=p16frKJLVi0 http://www.youtube.com/watch?v=p16frKJLVi0 by Noah Snavely, Steve Seitz, and Rick Szeliski

10. Optical character recognition Digit recognition, AT&T labs http://www.research.att.com/~yannhttp://www.research.att.com/~yann/ Technology to convert scanned docs to text If you have a scanner, it probably came with OCR software License plate readers http://en.wikipedia.org/wiki/Automatic_number_plate_recognition

11. Face detection Many new digital cameras now detect faces –Canon, Sony, Fuji, …

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

13. 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… “

14. Face recognition Who is she?

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

16. Login without a password… Fingerprint scanners on many new laptops, other devices Face recognition systems now beginning to appear more widely http://www.sensiblevision.com/ http://www.sensiblevision.com/

17. Object recognition (in mobile phones) This is becoming real: – Microsoft Research –Point & Find, Nokia, NTT DocomoPoint & FindNokiaNTT Docomo

18. The Matrix movies, ESC Entertainment, XYZRGB, NRC Special effects: shape capture Bullet time: http://www.youtube.com/watch?v=J5ryLMZTO5M

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

20. Sports Sportvision first down line Nice explanation on www.howstuffworks.comexplanation http://www.youtube.com/watch?v=UyPU2l9rdvo

21. Smart cars Mobileye –Vision systems currently in high-end BMW, GM, Volvo models –By 2010: 70% of car manufacturers. –Video demoVideo demo

22. Vision-based interaction (and games) 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!Lee’s work at CMU multi-touch display DigimaskDigimask: put your face on a 3D avatar. “Game turns moviegoers into Human Joysticks”, “Game turns moviegoers into Human Joysticks”, CNET Camera tracking a crowd, based on this work.this work

23. Vision-based HCI Reatrix: http://www.youtube.com/watch?v=QzsQKULMbiU http://www.youtube.com/watch?v=QzsQKULMbiU

24. Gaming Sony EyetoyMicrosoft Natal http://www.youtube.com/watch?v=AOXoh r4XE-4&feature=related http://www.youtube.com/watch?v=1BRSf CuLYHc

25. Motion capture Marker-based motion capture –http://www.youtube.com/watch?v=V0yT8mwg9nchttp://www.youtube.com/watch?v=V0yT8mwg9nc Organic motion http://www.organicmotion.com/

26. Looking at people Hand gesture Head pose Expression Identity http://www.youtube.com/watch?v=NwVBzx0LMNQ

27. Vision in space 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.Computer Vision on Mars NASA'S Mars Exploration Rover Spirit NASA'S Mars Exploration Rover Spirit captured this westward view from atop a low plateau where Spirit spent the closing months of 2007.

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

29. Robotics http://www.robocup.org/ NASA’s Mars Spirit Rover http://en.wikipedia.org/wiki/Spirit_rover

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

31. Digital comestics

32. Inpainting Bertalmio et al. SIGGRAPH 00

33. Debluring Fergus et al. SIGGRAPH 06

34. Digital photo albums Picasa, Flickr, Photobucket, etc. Categorization Tagging Search

35. Computational photography Image acquisition Hardware/software Optics Shuttle speed Novel sensors Multiple camera Multiple shots Multi flash Applications: high dynamic range imaging, super resolution, photomontage, panorama moasicing, debluring, light field, camera projector system…

36. Image and video search Google YouTubes Microsoft Yahoo

37. 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 applications in the next 5 years To learn more about vision applications and companies –David Lowe maintains an excellent overview of vision companiesDavid Lowe http://www.cs.ubc.ca/spider/lowe/vision.html Confluence of vision, graphics, learning, sensing and signal processing

38. Software and hardware Algorithms: processing images and videos Camera: acquiring images/videos Embedded system

39. Topics Image formation: camera model, camera calibration, radiometry, color, shading Early vision: stereopsis, structure from motion, illumination, reflectance, shape from X, texture Mid-level vision: segmentation, grouping, Kalman filter, particle filter, shape representation High-level vision: correspondence, matching, object detection, object recognition, visual tracking Recent topics: image and video retrieval, internet vision

40. Related topics

41. Textbooks and references Textbook –Computer Vision: A Modern Approach, David Forsyth and Jean PonceComputer Vision: A Modern Approach –Computer Vision: Algorithms and Applications (draft), Richard SzeliskiComputer Vision: Algorithms and Applications Reference for background study: –Introductory Techniques for 3-D Computer Vision, Emanuele Trucco and Alessandro Verri –Multiple View Geometry in Computer Vision, Richard Hartley and Andrew Zisserman –An Invitation to 3-D Vision by Yi Ma, Stefano Soatto, and Jana Kosecka –Robot Vision, Berthold Horn –Learning OpenCV: Computer Vision with OpenCV Library, Gary Bradski and Adrian Kaehler Reading assignments will be from the text and additional material that will be handed out or made available on the web page All lecture slides will be available on the course website http://faculty.ucmerced.edu/mhyang/course/cse274/index.htm

42. Grading Based on projects No midterm or final 20% Homework 40% Programming assignments 40% Term project

43. Project 1: features

44. Project 2: Lucas-Kande Tracker http://www.youtube.com/watch?v=yoQ8pSXrl4g

45. Project 3: object detection

46. Term Project Open-ended project of your choosing Oral presentation –Midterm presentation –Final presentation and demo Publish your results

47. General Comments Prerequisites—these are essential! –Data structures –A good working knowledge of MATLAB, C, and C++ programming –Linear algebra –Vector calculus Course does not assume prior imaging experience –computer vision, image processing, graphics, etc.

48. Acknowledgements Slides –David Forsyth and Jean Ponce –Richard Szleski and Steve Seitz