this is an introduction about computer vision

1. Computer Vision
BY : Mahmoud Hussein

2. What is Computer Vision?
 Make computers understand images and videos
 What kind of scene?
 Where are the cars ?
 How far is the building?

3. What is Computer Vision?
 Make computers understand images and videos
 Why is this happening?
 What is important ?
 What will I see?

5. Visual data on the Internet
 Flickr
 10+ billion photographs
 60 million images uploaded a month
 Facebook
 250+ billion
 300 million a day
 Instagram
 55 million a day
 YouTube
 100 hours uploaded every minute

6. Too big for humans
Need automatic tools to access and analyze visual
data!

8. Vision is Really Hard
 Vision is an amazing feature 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

9. Why is Computer Vision Hard?

10. What did you see?
 Where this picture was taken?
 How many people are there?
 What are they doing?
 What object the person on the left standing on?
 Why this is a funny picture?

11. Why is Computer Vision Hard?

12. Why is Computer Vision Hard?

13. Why is Computer Vision Hard?

14. Why is Computer Vision Hard?

15. Why is Computer Vision Hard?

16. Computer: okay, it’s a funny picture

17. Computer Vision Matters
safety Health security
comfort fun Access

18. History of Computer Vision
 “In 1966, Minsky hired a first-year, undergraduate student and assigned him a
problem to solve over the summer:
 Connect a camera to a computer and get the machine to describe what it sees.
Marvin
Minsky, MIT
Turing award,
1969

19. 1960’s: interpretation of synthetic worlds

20. 1970’s: some progress on interpreting
selected images

21. 1980’s: ANNs come and go; shift toward
geometry
and increased mathematical rigor

22. 1990’s: face recognition; statistical analysis
in vogue

23. 2000’s: broader recognition; large annotated
datasets available; video processing starts

24. 2010’s: resurgence of deep learning
[AlexNet NIPS
2012]
[DeepFace
CVPR 2014]
[DeepPose
CVPR 2014]
[Show, Attend and Tell
ICML 2015]

25. 2020’s: autonomous vehicles

26. 2030’s: robot uprising?

27. Examples of Computer Vision Applications
 How is computer vision used today?
 Face detection
 Most digital cameras and smart phones detect faces (and more)
 For smart focus, exposure compensation, and cropping

28. Face recognition

29. Face Landmark Alignment

30. Smile Detection

31. Eye contact detection

32. Optical Character Recognition (OCR)
 Technology to convert scanned docs to text
 If you have a scanner, it probably came with OCR software

33. Computer vision in sports

34. Computer vision in sports

35. Computer vision in sports

36. Matching street clothing photos in online Shops

37. Microsoft PhotoSynth: Photo Tourism

38. Style transfer

39. Interactive Games: Kinect

40. Industrial robots

41. Mobile robots

42. Computer vision for healthcare

43. Fundamentals of Computer Vision
 Light
 What an image records
 Matching
 How to measure the similarity of two regions
 Alignment
 How to align points
 Geometry
 How to relate world coordinates and image coordinates
 Categorization
 What similarities are important?

44. Image formation

45. Image filtering
 Linear filtering: function is a weighted sum/difference of pixel values
 Really important!
 Enhance images
 Denoise, smooth, increase contrast, etc.
 Extract information from images
 Texture, edges, distinctive points, etc.
 Detect patterns
 Template matching

46. What is OpenCV?
 Open source Computer Vision library
 Cross-platform
 C/C++ API
 Originally developed by Intel
 Optimized and intended for real-time applications
 More than 500 functions available

47. Uses
 Human-Computer Interaction (HCI)
 Object Identification
 Object Recognition
 Face Recognition
 Motion Tracking
 Image Processing
 Mobile Robotics
 … and so on.

48. OpenCV modules (I)
 Core
 basic structures and algorithms
 Imgproc
 Image Processing algorithms
 Highgui
 built-in simple UI
 Video
 video analysis

49. OpenCV modules (II)
 Calib3d
 camera calibration and 3D reconstruction
 Features2d
 2D features framework
 Object detection
 detection of objects and other items

50. OpenCV modules (III)
 Ml
 machine learning classes used for statistical classification, regression and clustering
of data
 Gpu
 GPU-accelerated algorithms