Lecture 05

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Lecture 05

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

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