1. Projective geometry ECE 847: Digital Image Processing Stan Birchfield Clemson University

2. Lines A line in 2D is described by two parameters: But vertical lines? Only two parameters are sufficient, but requires nonlinear formulation: slopey-intercept almost ^

3. Lines A better parameterization can represent all lines: Here the line is represented by 3 parameters: But nonzero scalar multiple does not change the equation: So we have only 2 degrees of freedom To make this work, we have to introduce a non-intuitive definition: I.e., the vector u and its scalar multiple are the same

4. Lines While we are at it, let us put the point into a vector, too: Which leads to the beautiful expression: Nonzero scalar multiple also does not change the point: So we introduce an analogous non-intuitive definition:

5. Example Ques: What does the vector [4, 6, 2] T represent? Ans: It depends. –If the vector is a 2D point, then the point is (4/2, 6/2) = (2, 3) -- divide by 3 rd coordinate –If the vector is a 2D line, then the line is 4x + 6y + 2 = 0, or 2x + 3y + 1 = 0 Points and lines are represented in the same way. Context determines which.

6. Lines Ques.: Is the point p on the line u? Ans: Check whether p T u = 0 Ques.: Which line passes through two points p 1 and p 2 ? Ans.: Compute u = p 1 x p 2 Ques.: Which points lies at the intersection of two lines? Ans.: Compute p = u 1 x u 2

7. Euclidean transformation 2D Euclidean transformation: is more conveniently represented as Again, we use 3 numbers to represent 2D point (These are homogeneous coordinates)

8. Perspective projection Nonlinear perspective projection can be replaced by linear equation where (x,y,w) T are homogeneous coordinates of (u,v,1) T :

9. Recap Homogeneous coordinates of 2D point (x,y) T are p=(wx,wy,w) T where w ≠ 0 We have seen three reasons for homogeneous coordinates: –simple representation of points and lines, no special cases –simple representation of Euclidean transformation –simple representation of perspective projection

10. Q & A Questions: –Is there a unifying theory to explain homogeneous coordinates? –How can they be extended to 3D? –Are they useful for anything else? Answers: –Projective geometry –Useful for planar warping, 3D reconstruction, image mosaicking, camera calibration, etc.

11. Euclidean  Projective Start with 2D Euclidean point (x,y) To convert to Projective, –Append 1 to the coordinates: p=(x,y,1) –Declare equivalence class: p=  p,  ≠0 To convert back to Euclidean, –Divide by last coordinate: (u, v, w)  (u/w, v/w) x=u/w, y=v/w

12. Ideal points What if last coordinate is zero? (u,v,0) Cannot divide by zero Projective plane contains more points than the Euclidean plane: –All Euclidean planes, plus –Points at infinity (a.k.a. ideal points) All ideal points lie on ideal line: (0, 0, 1)

13. Are ideal points special? In pure projective geometry, there is no distinction between real points and ideal points –Transformations will often convert one to another –We will freely make use of this, and often ignore the distinction However, distinction is necessary to convert back to Euclidean –Distinction will be made when we need to interpret results

14. Geometries Every geometry has –transformations –invariants

15. Stratification of geometries Euclidean similarity affine projective

16. Ray space

17. Unit hemisphere

18. Augmented affine plane

19. Homogeneous coordinates

20. Image formation 3D world point is (X,Y,Z,W) T 2D image point is (x,y,w) T Therefore, perspective projection is a 3x4 matrix P

21. Perspective projection Camera calibration matrix K

22. Homography Simple case is projection from plane to plane –Can be either world plane to image plane, or –image plane to another image plane, or –world plane to another world plane, –etc. 3x3 matrix is a projective transformation Called a homography

23. Euclidean homography Needs K

24. Essential and fundamental matrices

25. Relationship b/w FM and H Fundamental matrix and homography

26. How to compute homography Direct Linear Transform

27. Normalization Important

28. Stratification EuclideanProjectiveAffineSimilarity ideal lineabsolute pointsone length allow scale allow parallel projection allow perspective projection

29. Conics

30. Circular points aka absolute points

31. Absolute conic

32. Image of the absolute conic

33. Calibration using intersecting circles

34. Calibration using squares

35. Calibration using vanishing points

36. Calibration algorithm

37. Tsai’s calibration routine

38. Radial alignment constraint

39. What can you do with a calibrated camera