Lecture 06: Features

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Lecture 06: Features

  1. 1. Introduction to RoboticsFeatures<br />February 22, 2010<br />
  2. 2. Review: Vision<br />Convolution-based filters<br />Thresholds<br />Morphological operators<br />Goal: condensing information<br />OpenCV morphology demo<br />
  3. 3. Example: Edge Detection<br />OpenCV example edge<br />
  4. 4. Today: higher level features<br />Features allow to reason about the environment<br />Where am I<br />Where can I go<br />What is this<br />Where is this<br />N.B. Features can be extracted from ANY sensor<br />
  5. 5. Example: Visual Servoing/Grasping<br /><ul><li>Servo to fruit using image Jacobian
  6. 6. Rely on radius estimate for depth
  7. 7. Close gripper / retract arm when arrived</li></ul>2<br />u, v<br />w<br />F. Chaumette and S. Hutchinson, “Visual servo control part i: Basic approaches,” Robotics & Automation Magazine, vol. 13, no. 4, pp. 82–90<br />Function of arm kinematics<br />
  8. 8. Detection of Fruits<br />Objects are defined by features<br />Simple: filters “vote” for object locations<br />Depth estimated from radius<br />Color<br />Sobel<br />Spectral<br />Highlights<br />
  9. 9. Segmentation<br /><ul><li>Pyramid, mean-shift, graph-cut
  10. 10. Here: Watershed</li></ul>Gary Bradski (c) 2008<br />7<br />7<br />
  11. 11. Watershed algorithm<br />http://cmm.ensmp.fr/~beucher/wtshed.html<br />Demo OpenCVpyramid_segmentation<br />
  12. 12. Contours <br />Gary Bradski (c) 2008<br />9<br />9<br />
  13. 13. From points to geometry<br />Least-Square Fitting<br />Least-Squares Fitting of Circles and Ellipses, Walter Gander, Gene H. Golub, Rolf Strebel.<br />Demo: OpenCVconvexhull, squares<br />
  14. 14. Hough Transform<br />Demo: OpenCVhoughlines<br />
  15. 15. Hough Transform<br />Source: K. Grauma / D. Scaramuzza<br />
  16. 16. So far<br />Low-level image features<br />Convolution-based<br />Edge detection<br />Color detection<br />Watershed transform<br />Hough Transform<br />Morphology<br />What about convolution with more complex features?<br />
  17. 17. Face Detection with Viola-Jones Rejection Cascade and Boosting<br />14<br />14<br /><ul><li>Select features using learning (AdaBoost)
  18. 18. Narrow down objects using detection cascade</li></ul>by Viola & Jones<br />Robust Real-time Object Detection. Paul Viola Michael Jones. 2nd Int. Workshop on statistical and computational theories of vision – Modeling, Learning, Computing and Sampling, 2001.<br />by Gary Bradski<br />
  19. 19. Example: tomato detection<br />Learn dominant features from labeled set<br />Take random features<br />Learn features that generalize best<br />Detect features using convolution<br />Features “vote” on object centroid<br /><ul><li> good for partially occluded objects</li></ul>A. Torralba, K. Murphy, and W. Freeman, “Sharing features: efficient<br />boosting procedures for multiclass object detection,” in Proceedings<br />of the IEEE Computer Society Conference on Computer Vision and<br />Pattern Recognition (CVPR), 2004, pp. 762–769.<br />
  20. 20. Unique Point features<br />Example: D. Scaramuzza<br />
  21. 21. Harris corner detection<br />Corners: edge gradients in two directions<br />Idea: corners are repeatable and distinctive<br />MATLAB example: Harris Corner Detectorby Ali Ganoun<br />C.Harris and M.Stephens. A Combined Corner and Edge Detector.“ Proceedings of the 4th Alvey Vision Conference: pages 147--151.<br />
  22. 22. Harris corner detector<br />Investigate gradients in moving window<br />Flat regions: no change<br />in any direction<br />Edge: change along edgedirection<br />Corner: change alongtwo directions<br />Images: A. Efros<br />
  23. 23. Harris corner detector<br />Corners are invariant to rotation of the image, but distance-based matching is NOT<br />Corners are NOT invariant to scale<br />Corners are NOT invariant to illumination<br />D. Scaramuzza<br />
  24. 24. SIFT detector<br />Scale-free detector<br />Key idea: average intensity will be the same independent of rotation and scale<br />D. Scaramuzza<br />
  25. 25. Approach: Difference of Gaussians<br />D. Scaramuzza<br />
  26. 26. Performance<br />D. Scaramuzza<br />K.Mikolajczyk, C.Schmid. “Indexing Based on Scale Invariant Interest Points”. ICCV 2001.<br />
  27. 27. Algorithm<br />Find common maxima in scaled DoG images<br />Extract regional keypoint descriptor<br />Store/Compare descriptor in database<br />D. Scaramuzza<br />http://www.cs.ubc.ca/~lowe/keypoints/<br />
  28. 28. Project Assignments<br />4-5 groups<br />1-2 graduate students per group<br />Balance of CS/EE/ME and AE students<br />Goal: implement a controller for RobotStadium<br />Grad students: independent project focusing on one aspect of the controller<br />
  29. 29. Homework<br />Read chapter 5 -&gt; Section 5.5 (pages 181-212)<br />

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