Together, Image Processing Toolbox™ and Computer Vision Toolbox™ offer four image registration solutions: interactive registration with the Registration Estimator app, intensity-based automatic image registration, control point registration, and automated feature matching. For help selecting which approach to use, 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|
|Register 2-D images using median threshold bitmaps|
|Normalized 2-D cross-correlation|
|Mattes mutual information metric configuration|
|Mean square error metric configuration|
|Regular step gradient descent optimizer configuration|
|One-plus-one evolutionary optimizer configuration|
Control Point Registration
Register Images Interactively
- Register Images Using Registration Estimator App
Align a pair of images using feature-based registration techniques in the Registration Estimator app.
- Techniques Supported by Registration Estimator
The Registration Estimator app provides algorithms for feature-based, intensity-based, and nonrigid registration.
Register Images Using Intensity-Based Optimization
- Intensity-Based Automatic Image Registration
Intensity-based automatic image registration uses a similarity metric, an optimizer, and a transformation type to register two images iteratively.
- Create an Optimizer and Metric for Intensity-Based Image Registration
Select an image metric and an optimizer suitable for either monomodal or multimodal images.
- Use Phase Correlation as Preprocessing Step in Registration
Use phase correlation to estimate an initial transformation when images are severely misaligned.
- Registering an Image Using Normalized Cross-Correlation
Determine the translation needed to align a cropped subset of an image with the larger image.
Register Images Using Control Point Mapping
- Control Point Registration
To determine the parameters of a geometric transformation, you can pick corresponding pairs of points in two images.
- Geometric Transformation Types for Control Point Registration
Control point registration can infer the parameters for similarity, affine, projective, polynomial, piecewise linear, and local weighted mean transformations.
- Control Point Selection Procedure
To specify control points in a pair of images interactively, use the Control Point Selection Tool.
- Use Cross-Correlation to Improve Control Point Placement
Fine-tune your control point selections using cross-correlation.