General introduction to computer vision

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  • 1. CS 591 E / CS 791 L (CRN: 18390 / 18490) Computer Vision Instructor: Guodong Guo [email_address]
  • 2. Welcome!
    • Introductions
    • Administrative Matters
    • Course Outline
    • Applications of Computer Vision
    • Computer Vision Focus
    • Computer Vision Publications
      • Journals
      • Conferences
  • 3. Instructor
    • Guodong Guo
    • Ph.D. in CS from UW-Madison
    • http://www.cs.wisc.edu/~gdguo
    • Major Research Interest
      • Computer Vision, Machine Learning, Pattern Recognition, Biometrics, Multimedia, and HCI
  • 4. About You …
    • What do you know already?
      • C/C++ (Visual C++)
      • Matlab
      • Images
      • OpenCV
      • http://sourceforge.net/projects/opencvlibrary/
      • Install OpenCV in your PC or laptop,
      • Read the manual introduction
      • Try to load and save images (homework #0)
  • 5. Outline
    • Introductions
    • Administrative Matters
    • Course Outline
    • Applications of Computer Vision
    • Computer Vision Focus
    • Computer Vision Publications
  • 6. Meeting Times
    • Lectures
      • M 17:00-19:30 pm
      • Room ESB-E 449
    • Office hours
      • TR 1:00-2:00 pm (ESB 753)?
      • Or by appointment
  • 7. Grading
    • The final grade depends on:
      • Homework and programming assignments: 40%
      • Exams (Midterm): 40%
      • Final project (may include class presentation): 20%
      • Class participation: (-5%, if absent >= 3times)
      • Extra: 1~10% (for creative ideas, paper submission, etc.)
  • 8. Textbook
    • Computer Vision: A Modern Approach , 2 th Edition, by David Forsyth and Jean Ponce, Prentice Hall, 2003
  • 9. Look at the Syllabus
    • Course Objectives
    • Expected learning outcomes
    • Detailed list of topics (maybe updated)
  • 10. Outline
    • Introductions
    • Administrative Matters
    • Course Outline
    • Applications of Computer Vision
    • Computer Vision Focus
    • Computer Vision Publications
  • 11. What is Computer Vision?
    • Given an image or more, extract properties of the 3D world
    • Traffic scene
    • Number of vehicles
    • Type of vehicles
    • Location of closest obstacle
    • Assessment of congestion
  • 12. Computer Vision vs. Graphics
    • 3D  2D implies information loss
    • sensitivity to errors
    • need for models
    graphics vision
  • 13. Computer Vision vs. Biometrics
    • Biometrics comprises methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits
      • Physiological are related to the shape of the body, e.g., fingerprint, face recognition, DNA, hand and palm geometry, iris recognition, which has largely replaced retina, and odor/scent
      • Behavioral are related to the behavior of a person, e.g., typing rhythm, gait, and voice
  • 14. Computer Vision vs. Biometrics
    • Biometrics is a branch of Computer Vision
    • The development of Biometrics depends on Computer Vision techniques
  • 15. Computer Vision vs. Machine Learning
    • Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to change behavior based on data, such as from sensor data or databases (from Wikipedia)
    • A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data.
  • 16. Computer Vision vs. Machine Learning
    • Machine Learning is very useful for Computer Vision (e.g., learning for vision)
    • Computer Vision is more than just learning
      • Modeling
      • Example based learning
    • In Machine Learning, it usually does not care about how to obtain the data or sensors
    • In Computer Vision, we care how to obtain the visual data (sensor design, active vision), how to represent the visual data, and others
  • 17. Vision
    • Vision is the process of discovering what is present in the world and where it is by looking.
  • 18. Computer Vision
    • Computer Vision is the study of analysis of pictures and videos in order to achieve results similar to those as by people.
  • 19. Why Computer Vision
    • An image is worth 1000 words
    • Many biological systems rely on vision
    • The world is 3D and dynamic
    • Cameras and computers are cheap
  • 20. Computer Vision Examples
    • Finding People in images
    • Problem 1: Given an image I
    • Question: Does I contain an image of a person?
  • 21. “ Yes” Instances
  • 22. “ No” Instances
  • 23. Some Computer Vision Topics
  • 24. Imaging Geometry
  • 25. Camera Modeling
    • Pinhole Cameras
    • Lenses
    • Camera Parameters and Calibration
  • 26. Image Filtering and Enhancing
    • Linear Filters and Convolution
    • Image Smoothing
    • Edge Detection
    • Pyramids
  • 27. Image Filtering and Enhancing (cont.)
  • 28. Region Segmentation
  • 29. Color
  • 30. Texture
  • 31. Image Restoration Original Synthetic
  • 32. Perceptual Organization
  • 33. Perceptual Organization
  • 34. Shape Analysis
  • 35. Stereo
  • 36. Motion and Optical Flow
  • 37. High Level Vision
  • 38. Image Mosaic
  • 39. One Very Successful Example
    • Face detection in a digital camera
      • The camera detects faces in a scene and then automatically focuses (AF) and optimizes exposure (AE) and, if needed, flash output.
  • 40. Outline
    • Introductions
    • Administrative Matters
    • Course Outline
    • Applications of Computer Vision
    • Computer Vision Focus
    • Computer Vision Publications
  • 41. Applications
    • autonomous cars, planes, missiles, robots, ...
    • space exploration
    • aid to the blind, ASL recognition
    • manufacturing, quality control
    • surveillance, security, biometrics
    • image retrieval
    • medical imaging and analysis
    • ...
  • 42. Outline
    • Introductions
    • Administrative Matters
    • Course Outline
    • Applications of Computer Vision
    • Computer Vision Focus
    • Computer Vision Publications
  • 43. Computer Vision focuses on:
    • What information should be extracted?
    • How can it be extracted?
    • How should it be represented?
    • How can it be used to achieve the goal?
  • 44. Related disciplines
    • Image processing
    • Pattern recognition
    • Photogrammetry
    • Computer graphics
    • Artificial intelligence
    • Machine learning
    • Projective geometry
    • Control theory
  • 45. Active Research Topics
    • Object recognition
    • Human behavior analysis
    • Internet and computer vision
    • Biometrics and soft biometrics
    • Large scale 3D reconstruction (city level)
    • Medical image processing
    • Vision for robotics
  • 46. Outline
    • Introductions
    • Administrative Matters
    • Course Outline
    • Applications of Computer Vision
    • Computer Vision Focus
    • Computer Vision Publications
  • 47. 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
  • 48. Importance of CV
    • From these major journal rankings, we can see the importance of Computer Vision research in the whole areas of
      • Computer Science
      • Electrical Engineering
  • 49. Computer Vision Publications
    • Conferences
      • International Conference on Computer Vision (ICCV)
      • Conf. of Computer Vision and Pattern Recognition (CVPR)
      • Europe Conference on Computer Vision (ECCV)
  • 50.
    • Discussions and Questions