affine2d

2-D affine geometric transformation

Description

An affine2d object stores information about a 2-D affine geometric transformation and enables forward and inverse transformations.

Creation

You can create an affine2d object using the following methods:

  • imregtform — Estimate a geometric transformation that maps a moving image to a fixed image using similarity optimization.

  • imregcorr — Estimate a geometric transformation that maps a moving image to a fixed image using phase correlation.

  • fitgeotrans — Estimate a geometric transformation that maps pairs of control points between two images.

  • randomAffine2d — Create a randomized 2-D affine transformation.

  • The affine2d function described here.

Description

tform = affine2d creates an affine2d object with default property settings that correspond to the identity transformation.

example

tform = affine2d(T) sets the property T as the specified valid affine transformation matrix.

Properties

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Forward 2-D affine transformation, specified as a nonsingular 3-by-3 numeric matrix.

The matrix T uses the convention:

[x y 1] = [u v 1] * T

where T has the form:

 [a b 0;
  c d 0;
  e f 1];

The default of T is the identity transformation.

Data Types: double | single

Dimensionality of the geometric transformation for both input and output points, specified as the value 2.

Object Functions

invertInvert geometric transformation
isRigidDetermine if transformation is rigid transformation
isSimilarityDetermine if transformation is similarity transformation
isTranslationDetermine if transformation is pure translation
outputLimitsFind output spatial limits given input spatial limits
transformPointsForwardApply forward geometric transformation
transformPointsInverseApply inverse geometric transformation

Examples

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Create an affine2d object that defines a 30 degree rotation in the counterclockwise direction around the origin.

theta = 30;
tform = affine2d([ ...
    cosd(theta) sind(theta) 0;...
    -sind(theta) cosd(theta) 0; ...
    0 0 1])
tform = 
  affine2d with properties:

    Dimensionality: 2
                 T: [3x3 double]

Apply the forward geometric transformation to a point (10,0).

[x,y] = transformPointsForward(tform,10,0)
x = 8.6603
y = 5

Validate the transformation by plotting the original point (in blue) and the transformed point (in red).

plot(10,0,'bo',x,y,'ro')
axis([0 12 0 12])
axis square

Read and display an image.

I = imread('kobi.png');
imshow(I)

Create an affine2d transformation object that rotates images. The randomAffine2d function picks a rotation angle randomly from a continuous uniform distribution within the interval [35, 55] degrees.

tform1 = randomAffine2d('Rotation',[35 55]);

Rotate the image and display the result.

J = imwarp(I,tform1);
imshow(J)

The transformation object, tform1, rotates all images by the same amount. To rotate an image by a different randomly selected amount, create a new affine2d transformation object.

tform2 = randomAffine2d('Rotation',[-10 10]);
J2 = imwarp(I,tform2);
imshow(J2)

This example shows how to create a geometric transformation that can be used to align two images.

Create a checkerboard image and rotate it to create a misaligned image.

I = checkerboard(40);
J = imrotate(I,30);
imshowpair(I,J,'montage')

Define some matching control points on the fixed image (the checkerboard) and moving image (the rotated checkerboard). You can define points interactively using the Control Point Selection tool.

fixedPoints = [41 41; 281 161];
movingPoints = [56 175; 324 160];

Create a geometric transformation that can be used to align the two images, returned as an affine2d geometric transformation object.

tform = fitgeotrans(movingPoints,fixedPoints,'NonreflectiveSimilarity')
tform = 
  affine2d with properties:

    Dimensionality: 2
                 T: [3x3 double]

Use the tform estimate to resample the rotated image to register it with the fixed image. The regions of color (green and magenta) in the false color overlay image indicate error in the registration. This error comes from a lack of precise correspondence in the control points.

Jregistered = imwarp(J,tform,'OutputView',imref2d(size(I)));
figure
imshowpair(I,Jregistered)

Recover angle and scale of the transformation by checking how a unit vector parallel to the x-axis is rotated and stretched.

u = [0 1]; 
v = [0 0]; 
[x, y] = transformPointsForward(tform, u, v); 
dx = x(2) - x(1); 
dy = y(2) - y(1); 
angle = (180/pi) * atan2(dy, dx) 
angle = 29.7686
scale = 1 / sqrt(dx^2 + dy^2)
scale = 1.0003

Extended Capabilities

Introduced in R2013a