Mean square error metric configuration object
MeanSquares object describes a mean square
error metric configuration that you pass to the function
imregister to solve image registration
metric = registration.metric.MeanSquares() constructs
Value. To learn how value classes affect copy operations, see Copying Objects in the MATLAB® documentation.
This metric is an element-wise difference between
two input images. The ideal value is zero. You can examine the computed
values of mean square error if you enable
imregister. For example,
movingRegistered = imregister(moving,fixed,'rigid',optimizer,metric,'DisplayOptimization',true);
and use it to register two images captured with different sensors.
imregister doesn't support perspective
transformations. However it returns good results for this problem,
which uses a similarity transformation.
Read the images into the workspace.
fixed = imread('westconcordorthophoto.png'); moving = rgb2gray(imread('westconcordaerial.png'));
View the misaligned images.
Create the optimizer configuration object suitable for registering images from different sensors.
optimizer = registration.optimizer.OnePlusOneEvolutionary;
MeanSquares metric configuration
object. Even though the images came from different sensors, they have
an intensity relationship similar enough to use mean square error
as the similarity metric.
metric = registration.metric.MeanSquares
metric = registration.metric.MeanSquares This class has no properties.
of the optimizer to allow for more iterations.
optimizer.MaximumIterations = 1000;
Register the moving and fixed images.
movingRegistered = imregister(moving, fixed, 'similarity', optimizer, metric);
View the registered images.
figure; imshowpair(fixed, movingRegistered,'Scaling','joint');
uses an iterative process to register images. The metric you pass
imregister defines the image similarity metric
for evaluating the accuracy of the registration. An image similarity
metric takes two images and returns a scalar value that describes
how similar the images are. The optimizer you pass to
the methodology for minimizing or maximizing the similarity metric.
The mean squares image similarity metric is computed by squaring the difference of corresponding pixels in each image and taking the mean of the those squared differences.
imregconfig to construct
a metric configuration for typical image registration scenarios.