Together, Image Processing Toolbox™ and Computer Vision System Toolbox™ offer four image registration solutions: interactive registration with a Registration Estimator app, intensity-based automatic image registration, control point registration, and automated feature matching. For help in choosing among the approaches, see Approaches to Registering Images.
|Registration Estimator||Register 2-D grayscale images|
|Intensity-based image registration|
|Configurations for intensity-based registration|
|Estimate geometric transformation that aligns two 2-D or 3-D images|
|Estimate geometric transformation that aligns two 2-D images using phase correlation|
|Estimate displacement field that aligns two 2-D or 3-D images|
|2-D affine geometric transformation|
|3-D affine geometric transformation|
|2-D projective geometric transformation|
|2-D piecewise linear geometric transformation|
|2-D polynomial geometric transformation|
|2-D local weighted mean geometric transformation|
This example shows how to use the Registration Estimator app to align a pair of images.
Registration Estimator app provides ten algorithms for feature-based, intensity-based, and nonrigid registration.
Intensity-based automatic image registration uses a similarity metric, an optimizer, and a transformation type to register two images iteratively.
Select an image metric and an optimizer suitable for either monomodal or multimodal images.
Phase correlation is useful to estimate an initial transformation when images are severely misaligned.
This example shows how to align two multimodal MRI images to a common coordinate system using automatic intensity-based image registration.
To determine the parameters of a transformation, you can pick corresponding points in a pair of images.
To specify control points in a pair of images interactively, use the Control Point Selection Tool.
Fine-tune your control point selections using cross-correlation.
This example shows how to use control point mapping to register two images with a projective transformation.