# Lecture 19 Camera Matrices and Calibration

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1 Lecture 19 Camera Matrices and Calibration

2 Project Suggestions Texture Synthesis for In-Painting Section in Szeliski Text

3 Project Suggestions Image Stitching (Chapter 9)

4 Face Recognition Chapter 14

5 Center of Projection Image Plane Virtual Image Plane

6 Pinhole Camera Model This is the ideal model f Center of Projection

7 Moving on to 3D Unfortunately, we never get to see a 3D point We only see 2-D points So we will also need to model projection But this is awkward

8 Homogeneous Coordinates To get around this, we will introduce a new space of points, a projective space In this space of points any point is equivalent to

9 Now, Projection is easy to express We are just dropping the w term

10 The sensor is not the ideal image plane The origin is here Not here f Center of Projection

11 We modify the projection to account for that The origin is here Not here f Center of Projection

12 Basic Change to Projection Matrix We are just dropping the w term

13 We'll rewrite this in a more standard form This means that P is expressed in coordinates with the camera at the origin

14 Transformations We can think of this shift in two ways 1. The object moves 1inch in x and y

15 Transformations We can think of this shift in two ways 2. The axes moved up and to the left In both cases, the point is now farther away from the origin.

16 Transformations We can think of transformations as expressing the location of an object in a different coordinate system

17 How this relates to computer vision Let's assume we have a 3D model of this object How can we compute where the object will appear in the image taken by this camera? Can we just use the projection equations?

18 Can we just use the projection equations? Hint: Here's coordinate system that the object is defined on

19 Can we use the projection equations? But the projection equations assume that we are using the red coordinate system We need to figure out where the points lie in the camera's coordinate system

20 Lining up with the camera coordinate system That means transforming the model to have all of its points in the camera's coordinate system We assume that we can only move the camera or point it in different directions First, we translate

21 Lining up with the camera coordinate system First, we translate Remember, moving is the same (up to a sign) as staying put and moving the coordinate system

22 Lining up with the camera coordinate system Now we rotate

23 From the object's point of view

24 From the object's point of view Translate

25 From the object's point of view Rotate

26 Mathematically, We add the translation and rotation to this equation Which gives

27 Can Split that into Two Sets of Parameters Intrinsic Parameters: Relate Pixel Coordinates to Camera Reference Frame (Image from Forsyth and Ponce)

28 Can Split that into Two Sets of Parameters (x,y,z) v u Extrinsic Parameters: Relate position and orientation of camera to the world coordinate frame (Image from Forsyth and Ponce)

29 In terms of matrices Calibration Matrix Camera Matrix

30 The sensor is not the ideal image plane The physical retina may be slightly rotated f Center of Projection

31 The sensor is not the ideal image plane The physical retina may be slightly rotated The origin is here Not here f Center of Projection

32 So, the camera matrix may be complicated Can recover directly from data

33 Recovering the Camera Parameters We use a calibration target to get points in the scene with known 3D position Step 1: Get at least 6 point measurements Step 2: Recover Perspective Projection Matrix Step 3: From Projection Matrix, recover intrinsic and extrinsic parameters

34 Step 2: Estimating the Projection Matrix We can rewrite the perspective projection as (From Forsyth and Ponce) The vectors m are the rows of the perspective projection matrix Each 3D measurement gives us 2 constraints on the perspective projection matrix

35 Step 2: Estimating the Projection Matrix Since a 3x4 matrix has 12 unknowns, we need 6 points to generate enough constraints. Remember, matrices express linear systems (From Forsyth and Ponce)

36 Step 2: Estimating the Projection Matrix The constraints can be formulated as a matrix system (From Forsyth and Ponce)

37 Step 2: Estimating Projection Matrix What if you have more than 6 points, would that help? With more than 6 points, the system is, in general, overconstrained, so, in general, there will not be a unique solution. In this case we find the solution that best satisfies the system in the least-squares sense

38 Pseudo-Inverse Pseudo-Inverse is one way to find this vector Can compute this using Gaussian Elimination (Backslash in MATLAB) SVD QR Decomposition

39 Step 3:From Projection Matrix, recover intrinsic and extrinsic parameters Now we do algebra and recover the parameters if the perspective projection matrix A sample: See text for details

40 Easier Methods

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