Fit geometric transformation to control point pairs
tform = fitgeotrans(movingPoints,fixedPoints,transformationType)
tform = fitgeotrans(movingPoints,fixedPoints,'polynomial',degree)
tform = fitgeotrans(movingPoints,fixedPoints,'pwl')
tform = fitgeotrans(movingPoints,fixedPoints,'lwm',n)
tform = fitgeotrans(
PiecewiseLinearTransformation2D object to control
This transformation maps control points by breaking up the plane into
local piecewise-linear regions. A different affine transformation
maps control points in each local region.
tform = fitgeotrans(
LocalWeightedMeanTransformation2D object to control
The local weighted mean transformation creates a mapping, by inferring
a polynomial at each control point using neighboring control points.
The mapping at any location depends on a weighted average of these
n closest points are used to
infer a second degree polynomial transformation for each control point
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: T: [3×3 double] Dimensionality: 2
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
movingPoints— x- and y-coordinates of control points in the image you want to transform
x- and y-coordinates of control points in the image you want to transform, specified as an m-by-2 double matrix.
movingPoints = [11 11; 41 71];
fixedPoints— x- and y-coordinates of control points in the fixed image
x- and y- coordinates of control points in the fixed image, specified as an m-by-2 double matrix.
fixedPoints = [14 44; 70 81];
transformationType— Type of transformation
Type of transformation, specified as one of the following:
'projective'. For more information, see Transformation Types.
tform = fitgeotrans(movingPoints,fixedPoints,'nonreflectivesimilarity');
degree— Degree of the polynomial
Degree of the polynomial, specified as the integer 2, 3, or 4.
n— Number of points to use in local weighted mean calculation
Number of points to use in local weighted mean calculation,
specified as a numeric value.
n can be as small
as 6, but making
n small risks generating ill-conditioned
Transformation, returned as a transformation object. The type
of object depends on the transformation type. For example, if you
specify the transformation type
affine2d object. If you specify
The table lists all the transformation types supported by
order of complexity.
|Description||Minimum Number of Control Point Pairs||Example|
|Use this transformation when shapes in the moving image are unchanged, but the image is distorted by some combination of translation, rotation, and scaling. Straight lines remain straight, and parallel lines are still parallel.||2|
|Same as ||3|
|Use this transformation when shapes in the moving image exhibit shearing. Straight lines remain straight, and parallel lines remain parallel, but rectangles become parallelograms.||3|
|Use this transformation when the scene appears tilted. Straight lines remain straight, but parallel lines converge toward a vanishing point.||4|
|Use this transformation when objects in the image are curved. The higher the order of the polynomial, the better the fit, but the result can contain more curves than the fixed image.|
6 (order 2)
10 (order 3)
15 (order 4)
|Use this transformation when parts of the image appear distorted differently.||4|
|Use this transformation (local weighted mean), when the distortion varies locally and piecewise linear is not sufficient.||6 (12 recommended)|
 Goshtasby, Ardeshir, "Piecewise linear mapping functions for image registration," Pattern Recognition, Vol. 19, 1986, pp. 459-466.
 Goshtasby, Ardeshir, "Image registration by local approximation methods," Image and Vision Computing, Vol. 6, 1988, pp. 255-261.
Usage notes and limitations:
This function supports the generation of C code using MATLAB® Coder™. For more information, see Code Generation for Image Processing.
When generating code, the
must be a compile-time constant and only the following transformation
types are supported: