![Smart cars
• Mobileye [wiki article]
– Vision systems currently in many car models
Slide content courtesy of Amnon Shashua](https://image.slidesharecdn.com/vision-230619084926-09a89fdd/85/vision-ppt-24-320.jpg)
vision.ppt
- 1. Introduction to Computer Vision Based on slides by Jinxiang Chai, Svetlana Lazebnik, Guodong Guo Assembled and modified by Longin Jan Latecki September 2012
- 2. What is Computer Vision? • Computer vision is the science and technology of machines that see. • Concerned with the theory for building artificial systems that obtain information from images. • The image data can take many forms, such as a video sequence, depth images, views from multiple cameras, or multi-dimensional data from a medical scanner
- 3. Computer Vision Make computers understand images and videos. What kind of scene? Where are the cars? How far is the building? …
- 4. Components of a computer vision system Lighting Scene Camera Computer Scene Interpretation Srinivasa Narasimhan’s slide
- 5. Computer vision vs human vision What we see What a computer sees
- 6. Vision is really hard • Vision is an amazing feat of natural intelligence – Visual cortex occupies about 50% of Macaque brain – More human brain devoted to vision than anything else Is that a queen or a bishop?
- 7. Vision is multidisciplinary From wiki Computer Graphics HCI
- 8. Why computer vision matters Safety Health Security Comfort Access Fun
- 9. A little story about Computer Vision In 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to “spend the summer linking a camera to a computer and getting the computer to describe what it saw”. We now know that the problem is slightly more difficult than that. (Szeliski 2009, Computer Vision)
- 10. A little story about Computer Vision In 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to “spend the summer linking a camera to a computer and getting the computer to describe what it saw”. We now know that the problem is slightly more difficult than that. Founder, MIT AI project
- 11. A little story about Computer Vision In 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to “spend the summer linking a camera to a computer and getting the computer to describe what it saw”. We now know that the problem is slightly more difficult than that. Image Understanding
- 12. Ridiculously brief history of computer vision • 1966: Minsky assigns computer vision as an undergrad summer project • 1960’s: interpretation of synthetic worlds • 1970’s: some progress on interpreting selected images • 1980’s: ANNs come and go; shift toward geometry and increased mathematical rigor • 1990’s: face recognition; statistical analysis in vogue • 2000’s: broader recognition; large annotated datasets available; video processing starts; vision & graphis; vision for HCI; internet vision, etc. Guzman ‘68 Ohta Kanade ‘78 Turk and Pentland ‘91
- 13. How vision is used now • Examples of state-of-the-art
- 14. Optical character recognition (OCR) Digit recognition, AT&T labs http://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
- 15. Face detection • Many new digital cameras now detect faces – Canon, Sony, Fuji, …
- 16. Smile detection Sony Cyber-shot® T70 Digital Still Camera
- 17. 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… “
- 18. Vision-based biometrics “How the Afghan Girl was Identified by Her Iris Patterns” Read the story wikipedia
- 19. 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/
- 20. Object recognition (in mobile phones) Point & Find, Nokia Google Goggles
- 21. The Matrix movies, ESC Entertainment, XYZRGB, NRC Special effects: shape capture
- 22. Pirates of the Carribean, Industrial Light and Magic Special effects: motion capture
- 23. Sports Sportvision first down line Nice explanation on www.howstuffworks.com http://www.sportvision.com/video.html
- 24. Smart cars • Mobileye [wiki article] – Vision systems currently in many car models Slide content courtesy of Amnon Shashua
- 26. Interactive Games: Kinect • Object Recognition: http://www.youtube.com/watch?feature=iv&v=fQ59dXOo63o • Mario: http://www.youtube.com/watch?v=8CTJL5lUjHg • 3D: http://www.youtube.com/watch?v=7QrnwoO1-8A • Robot: http://www.youtube.com/watch?v=w8BmgtMKFbY • 3D tracking, reconstruction, and interaction: http://research.microsoft.com/en- us/projects/surfacerecon/default.aspx
- 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. NASA'S Mars Exploration Rover Spirit captured this westward view from atop a low plateau where Spirit spent the closing months of 2007.
- 28. Industrial robots Vision-guided robots position nut runners on wheels
- 29. Mobile robots http://www.robocup.org/ NASA’s Mars Spirit Rover http://en.wikipedia.org/wiki/Spirit_rover Saxena et al. 2008 STAIR at Stanford
- 30. Medical imaging Image guided surgery Grimson et al., MIT 3D imaging MRI, CT
- 31. Vision as a source of semantic information slide credit: Fei-Fei, Fergus
- 32. Object categorization sky building flag wall banner bus cars bus face street lamp slide credit: Fei-Fei, Fergus
- 33. Scene and context categorization • outdoor • city • traffic • … slide credit: Fei-Fei, Fergus
- 34. Qualitative spatial information slanted rigid moving object horizontal vertical slide credit: Fei-Fei, Fergus rigid moving object non-rigid moving object
- 35. Challenges: viewpoint variation Michelangelo 1475-1564 slide credit: Fei-Fei, Fergus
- 36. Challenges: illumination image credit: J. Koenderi
- 37. Challenges: scale slide credit: Fei-Fei, Fergus
- 38. Challenges: deformation Xu, Beihong 1943 slide credit: Fei-Fei, Fergus
- 39. Challenges: occlusion Magritte, 1957 slide credit: Fei-Fei, Fergus
- 41. Challenges: object intra-class variation slide credit: Fei-Fei, Fergus
- 42. Challenges: local ambiguity slide credit: Fei-Fei, Fergus
- 43. Challenges or opportunities? • Images are confusing, but they also reveal the structure of the world through numerous cues • Our job is to interpret the cues! Image source: J. Koenderin
- 44. Bottom line • Perception is an inherently ambiguous problem – Many different 3D scenes could have given rise to a particular 2D picture Image source: F. D
- 45. Bottom line • Perception is an inherently ambiguous problem – Many different 3D scenes could have given rise to a particular 2D picture • Possible solutions – Bring in more constraints ( or more images) – Use prior knowledge about the structure of the world • Need both exact measurements and statistical inference! Image source: F. D
- 46. Computer Vision vs. Graphics • 3D 2D implies information loss • sensitivity to errors • need for models graphics vision
- 47. Imaging Geometry
- 48. Camera Modeling • Pinhole Cameras • Lenses • Camera Parameters and Calibration
- 49. Image Filtering and Enhancing • Linear Filters and Convolution • Image Smoothing • Edge Detection
- 51. Color
- 52. Texture
- 53. Image Restoration / Nose Removal Original Synthetic
- 56. Shape Analysis
- 57. Computer Vision Publications • Journals – IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) • #1 IEEE, Thompson-ISI impact factor: 5.96 • #1 in both electrical engineering and artificial intelligence • #3 in all of computer science – Internal Journal of Computer Vision (IJCV) • ISI impact factor: 5.358, Rank 2 of 94 in “CS, artificial intelligence – IEEE Trans. on Image Processing – … • Conferences – Conf. of Computer Vision and Pattern Recognition (CVPR), once a year – International Conference on Computer Vision (ICCV), once every two years – Europe Conference on Computer Vision (ECCV), once every two years