Lecture 05
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Lecture 05 Presentation Transcript

  • 1. Introduction to RoboticsVision-based ranging and Optical Filters
    CSCI 4830/7000
    September 27, 2010
    NikolausCorrell
  • 2. Review: Sensing
    Important: sensors report data in their own coordinate frame
    Examples from the exercise
    Accelerometer of Nao
    Laser scanner
    Treat like forward kinematics
  • 3. Laser Scanner
  • 4. Today
    Perception using vision
    Range information from Vision
    Basic Image Processing
    Why is object recognition hard?
    -> “Computer Vision” with Jane Mulligan
  • 5. Range sensing
    Last week
    Laser scanner (phase shift)
    Infrared (path loss)
    Ultrasound (time-of-flight)
    Today
    Depth from focus
    Depth from Stereo
  • 6. Pin-Hole Camera
    A. Efros
  • 7. Pin-hole Model
  • 8. Aperture
  • 9. Thin Lens
    Objects need to have the right distance to be in focus -> Depth-from-Focus method
  • 10. Depth from Focus
    “in focus” + camera parameters
    = range
    How to test whether an image is “crisp” or “blurry”?
  • 11. Testing for focus
    Unit Step -> 2nd Derivative
    Intuition: Images with high contrast have steep edges!
  • 12. Convolution
    Calculate Laplacian / 2nd derivative by “convolving” image with 2D Kernel
    *
  • 13. Depth from Stereo
    Distance between stereo pair known + distance in the image -> distance to object
  • 14. Stereo Pairs
    Zero crossings of Laplacians of Gaussians
    Gaussians: blurred image (suppresses noise)
    Laplacians: edges
    Test how far similar edges are apart
    Epipolar constraints are given by the geometry of the Stereo pair
  • 15. Other example for Convolutions: Canny Edge Detector
    1.
    2.+3.
    4. Trace along ridges (non-maximum suppression)
    15
  • 16. Exercise: Thresholds
    16
    16
    http://homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm
    Screen shots by Gary Bradski, 2005
  • 17. Exercise: Morphological Operations Examples
    Morphology - applying Min-Max. Filters and its combinations
    Dilatation IB
    Opening IoB= (IB)B
    Erosion IB
    Image I
    Closing I•B= (IB)B
    TopHat(I)= I - (IB)
    BlackHat(I)= (IB) - I
    Grad(I)= (IB)-(IB)
  • 18.
  • 19. Why is Object Recognition Hard?The difference between seeing and perception.
    Gary Bradski, 2009
    19
    What to do?
    Maybe we should try to find edges ….
    Gary Bradski, 2005
  • 20.
    • Depth discontinuity
    • 21. Surface orientation discontinuity
    • 22. Reflectance discontinuity (i.e., change in surface material properties)
    • 23. Illumination discontinuity (e.g., shadow)
    Slide credit: Christopher Rasmussen
    20
    But, What’s an Edge?
  • 24. To Deal With the Confusion, Your Brain has Rules...That can be wrong
  • 25. We even see invisible edges…
  • 26. And surfaces …
  • 27. We need to deal with 3D Geometry
    24
    Perception is ambiguous … depending on your point of view!
    Graphic by Gary Bradski
  • 28. And Lighting in 3D
    Which square is darker?
  • 29. Lighting is Ill-posed …
    Perception of surfaces depends on lighting assumptions
    26
    Gary Bradski (c) 2008
    26
  • 30. Contrast
    27
    Which one is male and which one is female?
    Illusion by: Richard Russell,Harvard University
    Russell, R. (2009) A sex difference in facial pigmentation and its exaggeration by cosmetics. Perception, (38)1211-1219
  • 31. Frequency
  • 32. Color
    http://briantobin.info/2009/06/lost-and-found-visual-illusion.html
  • 33. Homework
    Read sections 4.2-5 (pages 145-180)
    Questionnaire on CU Learn
    Midterm: October 11 (during class)