4. Today: higher level features Features allow to reason about the environment Where am I Where can I go What is this Where is this N.B. Features can be extracted from ANY sensor
7. Close gripper / retract arm when arrived2 u, v w F. Chaumette and S. Hutchinson, “Visual servo control part i: Basic approaches,” Robotics & Automation Magazine, vol. 13, no. 4, pp. 82–90 Function of arm kinematics
8. Detection of Fruits Objects are defined by features Simple: filters “vote” for object locations Depth estimated from radius Color Sobel Spectral Highlights
13. From points to geometry Least-Square Fitting Least-Squares Fitting of Circles and Ellipses, Walter Gander, Gene H. Golub, Rolf Strebel. Demo: OpenCVconvexhull, squares
16. So far Low-level image features Convolution-based Edge detection Color detection Watershed transform Hough Transform Morphology What about convolution with more complex features?
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18. Narrow down objects using detection cascadeby Viola & Jones 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. by Gary Bradski
21. Harris corner detection Corners: edge gradients in two directions Idea: corners are repeatable and distinctive MATLAB example: Harris Corner Detectorby Ali Ganoun C.Harris and M.Stephens. A Combined Corner and Edge Detector.“ Proceedings of the 4th Alvey Vision Conference: pages 147--151.
22. Harris corner detector Investigate gradients in moving window Flat regions: no change in any direction Edge: change along edgedirection Corner: change alongtwo directions Images: A. Efros
23. Harris corner detector Corners are invariant to rotation of the image, but distance-based matching is NOT Corners are NOT invariant to scale Corners are NOT invariant to illumination D. Scaramuzza
24. SIFT detector Scale-free detector Key idea: average intensity will be the same independent of rotation and scale D. Scaramuzza
26. Performance D. Scaramuzza K.Mikolajczyk, C.Schmid. “Indexing Based on Scale Invariant Interest Points”. ICCV 2001.
27. Algorithm Find common maxima in scaled DoG images Extract regional keypoint descriptor Store/Compare descriptor in database D. Scaramuzza http://www.cs.ubc.ca/~lowe/keypoints/
28. Project Assignments 4-5 groups 1-2 graduate students per group Balance of CS/EE/ME and AE students Goal: implement a controller for RobotStadium Grad students: independent project focusing on one aspect of the controller
