Fundamentals of Computer Vision
COMP 558    Fall 2009
Instructor: Prof. Michael Langer

Lecture Notes

1. pinhole camera model (PDF)
   
2. image motion seen by moving camera (PDF)
   
3. homogeneous coordinates, camera model (PDF)
   
4. thin lens model (PDF)
   
5. lighting and reflectance (PDF)
   
6. color, image capture (PDF)
   
7. intro to linear systems (convolution) (PDF)
   
8. 1D Canny edge detection (PDF)
   
9. 2D Canny edge detection, Harris corners (PDF)
   
10. image registration (Lucas-Kanade) (PDF)
11. scale space 1 (normalized derivatives, blobs) (PDF) and (code)
    
12. scale space 2 (coarse to fine, SIFT) (PDF)
13. fitting lines (least squares, Hough, RANSAC) (PDF)
14. finding vanishing points (PDF)
15. shape from shading 1 (PDF)
16. shape from shading 2 (PDF)
17. shape from texture, focus (PDF)
18. camera calibration, least squares (PDF)
19. homographies, SVD (PDF)
20. structure from motion 1 (egomotion) (PDF)
21. structure from motion 2 (factorization) (PDF)
22. stereo 1 (epipolar constraints, fundamental matrix) (PDF)   song
23. stereo 2 (rectification, correspondence) (PDF)
24. stereo 3 (graph cuts) (PDF)