Lecture 05: Vision
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Lecture 05: Vision

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Lecture 05: Vision Lecture 05: Vision Presentation Transcript

  • Introduction to RoboticsPerception II
    CSCI 4830/7000
    February 15, 2010
    NikolausCorrell
  • Review: Sensing
    Important: sensors report data in their own coordinate frame
    Examples from the exercise
    Accelerometer of Nao
    Laser scanner
    Treat like forward kinematics
  • Today
    Perception using vision
    Practical angle:
    Why is vision hard
    Basic image processing
    How to combine image processing primitives into object recognition
    OpenCV / SwisTrack
  • Why is Vision Hard?The difference between seeing and perception.
    Gary Bradski, 2009
    4
    What to do?
    Maybe we should try to find edges ….
    Gary Bradski, 2005
    • Depth discontinuity
    • Surface orientation discontinuity
    • Reflectance discontinuity (i.e., change in surface material properties)
    • Illumination discontinuity (e.g., shadow)
    Slide credit: Christopher Rasmussen
    5
    But, What’s an Edge?
  • To Deal With the Confusion, Your Brain has Rules...That can be wrong
  • We even see invisible edges…
  • And surfaces …
  • We need to deal with 3D Geometry
    9
    Perception is ambiguous … depending on your point of view!
    Graphic by Gary Bradski
  • And Lighting in 3D
    Which square is darker?
  • Lighting is Ill-posed …
    Perception of surfaces depends on lighting assumptions
    11
    Gary Bradski (c) 2008
    11
  • Contrast
    12
    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
  • Frequency
  • Color
    http://briantobin.info/2009/06/lost-and-found-visual-illusion.html
  • Pin-hole Model
  • Pin-Hole Camera
    A. Efros
  • Aperture
  • Increasing Aperture: Lens
  • Thin Lens
    Objects need to have the right distance to be in focus -> Depth-from-Focus method
  • Thresholds
    20
    20
    http://homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm
    Screen shots by Gary Bradski, 2005
  • Canny Edge Detector
    21
    Gary Bradski (c) 2008
    21
  • 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)
  • Stereo Calibration
    Screen shots and charts by Gary Bradski, 2005
    Gary Bradski (c) 2008
    23
    23
  • 3D Stereo Vision
    Find Epipolar lines:
    Align images:
    Triangulate points:
    Depth:
  • Example: Tomato-Picking Robot
    Challenges
    Foliage
    Reflections
    Varying size and shape
    Varying color
    Partly covered fruits
    http://swistrack.sourceforge.net
    N. Correll, N. Arechiga, A. Bolger, M. Bollini, B. Charrow, A. Clayton, F. Dominguez, K. Donahue, S. Dyar, L. Johnson, H. Liu, A. Patrikalakis, T. Robertson, J. Smith, D. Soltero, M. Tanner, L. White, D. Rus. Building a Distributed Robot Garden. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1509-1516, St. Louis, MO.
  • Filter-based object recognition
    Filter image
    Sobel
    Hough transform
    Color
    Spectral highlights
    Size and shape
    Weighted sum of filters highlights object location
    Color
    Sobel
    Hough
    Spectral
    Highlights
  • Group exercise
    Object recognition
    Goal
    Players
    Ball
    Field
  • Homework
    Read sections 4.2-5 (pages 145-180)
    Questionnaire on CU Learn