CS 453 - Spring 2013

Computer Vision


Announcements

Final projects

Homework

  1. Homework 1, due Wednesday, 2/20. Solutions. Pinhole cameras.
  2. Homework 2, due Wednesday, 2/27. Solutions.
  3. Homework 3, due Friday 3/8. Solutions.
  4. Homework 4, due Friday, 3/15. Solutions.
  5. Homework 5, due Friday, 3/22. Solutions.
  6. Homework 6, due Wednesday, 4/3. Solutions.
  7. Homework 7, due Friday, 4/12. Solutions.
  8. Homework 8, due Monday, 4/22. Solutions. Stereo ranking results.
  9. Homework 9, due Monday, 4/29. Solutions. SFM results.
  10. Final project.

Lectures & Readings

  1. M 2/11 - Course info, introduction to computer vision (Sz 1)
  2. W 2/13 - Image formation, perspective projection, image library (Sz 2; HW 1)
  3. M 2/18 - Ideal vs. real images, run-encoding (SS 2.3-5)
  4. W 2/20 - Binary image analysis, filtering, connected components (SS 3.1-4; Sz 3.3.4)
  5. F  2/22 - Binary morphology, noise and smoothing (SS 3.5; Sz 3.3.2, 3.2)
  6. M 2/25 - Noise estimation, Gaussian smoothing, edge detection (TV 2.3.3; Sz 3.2.3, 4.2.1)
  7. W 2/27 - Image gradients, difference operators, Laplacian, Marr-Hildreth edge detector (Sz 4.2.1; TV 4)
  8. F  3/1   - Animal vision (Dan C.), Canny edge detector, (Sz 4.2.1; TV 4.2)
  9. M 3/4   - Edge detection summary, Hough transform (Sz 4.2.2, 4.3.2; TV 4)
  10. W 3/6   - Hough transform, least-squares fitting (Sz 4.3.2, A2; TV 5.2; SS 10.3.4, MathWorld)
  11. F  3/8   - HDR imaging (Miriam), HW 4 code (Sz 10.2)
  12. M 3/11 - Color (Sz 2.3.2; SS 6.1-4)
  13. W 3/13 - Feature detection and matching (Colby), geometric transforms (Sz. 4.1.1-3, 2.1.2)
  14. F  3/15 - Active contours / snakes (Nera), image warping, interpolation (Sz 5.1.1, 3.6.1)
  15. M 3/18 - Image compositing, homogeneous coordinates, homographies (Sz 3.1.3, 2.1.1-3)
  16. W 3/20 - Texture synthesis (Xi), 3D vision, projection matrix, camera parameters (Sz 10.5, 2.1.5)
  17. F  3/22 - JPEG and MPEG encoding (Paul), intrinsic vs. extrinsic parameters, camera calibration (Sz 2.1.5; TV 6.1, 6.3.1)
  18. M 4/1   - Exam info, camera calibration, SVD (TV 6.3.1, Sz A.1.1)
  19. W 4/3   - Exam review, intro to stereo (Sch 1.2, Sz 11.1)
  20. F  4/5   - Epipolar geometry, local correspondence, SSD (Sch 1.2, Sz 11.1, 11.3-4)
  21. M 4/8   - Stereo: SSD, SAD, NCC, disparity space (Sch 5.2, 6.1)
  22. W 4/10 - Image stabilization (Dan T.), Image matting (Greg), Middlebury Stereo Page
  23. F  4/12 - Stereo: a robust matching, shiftable windows, adaptive weights, cross-checking
  24. M 4/15 - Global matching, MRF stereo, dynamic programming stereo, (Sz 3.7.2, 5.5, 11.5, Middlebury MRF page)
  25. W 4/17 - Image segmentation (Teddy), motion, optical flow (Sz 5, 8.4; SS 9.3.4)
  26. M 4/22 - Feature tracking (Billy), structured light, hidden texture (Sz 4.1.4, 12.2, Middlebury flow page)
  27. W 4/24 - Tomasi and Kanade's SFM factorization method (Sz 7.3, A.1.1, paper, video)
  28. F  4/26 - Intro to OpenCV
  29. M 4/29 - Final project info, Intro to Recognition (Sz 14, UW CS576 slides: Object recognition and CVPR tutorial)
  30. W 5/1   - Shape from shading/texture (Matei), Shape from focus/defocus (Dew) Shape-from-X, (Sz 12.1, CAVE demos)
  31. F  5/3   - Face detection, principal component matching, "Eigenfaces" (Sz 14.1-2), UW CS576 slides: Face recognition)
  32. M 5/6   - Lab day
  33. W 5/8   - Computational photography (slides by Rick Szeliski)
  34. F  5/10 - Final project demos
  35. M 5/13 - Course summary and final exam info

Books on 2-hour reserve: Shapiro & Stockman (SS); Trucco & Verri (TV); Forsyth & Ponce; Nalwa; Sonka, Hlavac, & Boyle.

Resources online: