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Last 4 lectures Camera Structure HDR Image Filtering Image Transform.

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Presentation on theme: "Last 4 lectures Camera Structure HDR Image Filtering Image Transform."— Presentation transcript:

1 Last 4 lectures Camera Structure HDR Image Filtering Image Transform

2 Today Camera Projection Camera Calibration

3 Pinhole camera

4 Pinhole camera model (optical center) origin principal point P (X,Y,Z) p (x,y) The coordinate system –We will use the pin-hole model as an approximation –Put the optical center (Center Of Projection) at the origin –Put the image plane (Projection Plane) in front of the COP (Why?)

5 Pinhole camera model principal point

6 Pinhole camera model (optical center) origin principal point P (X,Y,Z) p (x,y) x y

7 Pinhole camera model (optical center) origin principal point P (X,Y,Z) p (x,y) x y

8 Intrinsic matrix non-square pixels (digital video) Is this form of K good enough?

9 Intrinsic matrix non-square pixels (digital video) skew Is this form of K good enough?

10 Intrinsic matrix non-square pixels (digital video) skew radial distortion Is this form of K good enough?

11 Distortion Radial distortion of the image –Caused by imperfect lenses –Deviations are most noticeable for rays that pass through the edge of the lens

12 Barrel Distortion No distortion Barrel Wide Angle Lens

13 Pin Cushion Distortion Pin cushion Telephoto lens No distortion

14 Modeling distortion To model lens distortion –Use above projection operation instead of standard projection matrix multiplication 2. Apply radial distortion 3. Apply focal length translate image center 1. Project (X, Y, Z) to “normalized” image coordinates Distortion-Free:With Distortion:

15 Camera rotation and translation extrinsic matrix

16 Two kinds of parameters internal or intrinsic parameters: focal length, optical center, skew external or extrinsic (pose): rotation and translation:

17 Other projection models

18 Orthographic projection Special case of perspective projection –Distance from the COP to the PP is infinite Image World –Also called “parallel projection”: (x, y, z) → (x, y)

19 Other types of projections Scaled orthographic –Also called “weak perspective” Affine projection –Also called “paraperspective”

20 Fun with perspective

21 Perspective cues

22

23 Fun with perspective Ames room

24 Forced perspective in LOTR Elijah Wood: 5' 6" (1.68 m)Ian McKellen 5' 11" (1.80 m)

25 Camera calibration

26 Estimate both intrinsic and extrinsic parameters Mainly, two categories: 1.Using objects with known geometry as reference 2.Self calibration (structure from motion)

27 Camera calibration approaches Directly estimate 11 unknowns in the M matrix using known 3D points (Xi,Yi,Zi) and measured feature positions (ui,vi)

28 Linear regression

29

30 Solve for Projection Matrix M using least- square techniques

31 Normal equation (Geometric Interpretation) Given an overdetermined system the normal equation is that which minimizes the sum of the square differences between left and right sides

32 Normal equation (Differential Interpretation) n x m, n equations, m variables

33 Normal equation Who invented Least Square? Carl Friedrich Gauss

34 Nonlinear optimization A probabilistic view of least square Feature measurement equations Likelihood of M given {(u i,v i )}

35 Optimal estimation Log likelihood of M given {(u i,v i )} It is a least square problem (but not necessarily linear least square) How do we minimize C?

36 Nonlinear least square methods

37 Least square fitting number of data points number of parameters

38 Nonlinear least square fitting

39 Function minimization It is very hard to solve in general. Here, we only consider a simpler problem of finding local minimum. Least square is related to function minimization.

40 Function minimization

41 Quadratic functions Approximate the function with a quadratic function within a small neighborhood

42 Function minimization

43 Computing gradient and Hessian Gradient Hessian

44 Computing gradient and Hessian Gradient Hessian

45 Computing gradient and Hessian Gradient Hessian

46 Computing gradient and Hessian Gradient Hessian

47 Computing gradient and Hessian Gradient Hessian

48 Searching for update h GradientHessian Idea 1: Steepest Descent

49 Steepest descent method isocontourgradient

50 Steepest descent method It has good performance in the initial stage of the iterative process. Converge very slow with a linear rate.

51 Searching for update h GradientHessian Idea 2: minimizing the quadric directly Converge faster but needs to solve the linear system

52 Recap: Calibration Directly estimate 11 unknowns in the M matrix using known 3D points (Xi,Yi,Zi) and measured feature positions (ui,vi) Camera Model:

53 Recap: Calibration Directly estimate 11 unknowns in the M matrix using known 3D points (Xi,Yi,Zi) and measured feature positions (ui,vi) Linear Approach:

54 Recap: Calibration Directly estimate 11 unknowns in the M matrix using known 3D points (Xi,Yi,Zi) and measured feature positions (ui,vi) NonLinear Approach:

55 Practical Issue is hard to make and the 3D feature positions are difficult to measure!

56 A popular calibration tool

57 Multi-plane calibration Images courtesy Jean-Yves Bouguet, Intel Corp. Advantage Only requires a plane Don’t have to know positions/orientations Good code available online! –Intel’s OpenCV library: –Matlab version by Jean-Yves Bouget: –Zhengyou Zhang’s web site:

58 Step 1: data acquisition

59 Step 2: specify corner order

60 Step 3: corner extraction

61

62 Step 4: minimize projection error

63 Step 4: camera calibration

64

65 Step 5: refinement


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