Image Registration

Align two images using intensity correlation, feature matching, or control point mapping

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.

Apps

Registration Estimator

Functions

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Automatic Registration

`imregister`	Intensity-based image registration
`imregconfig`	Configurations for intensity-based registration
`imregtform`	Estimate geometric transformation that aligns two 2-D or 3-D images
`imregcorr`	Estimate geometric transformation that aligns two 2-D images using phase correlation
`imregdemons`	Estimate displacement field that aligns two 2-D or 3-D images
`imregmtb`	Register 2-D images using median threshold bitmaps
`normxcorr2`	Normalized 2-D cross-correlation
`MattesMutualInformation`	Mattes mutual information metric configuration
`MeanSquares`	Mean square error metric configuration
`RegularStepGradientDescent`	Regular step gradient descent optimizer configuration
`OnePlusOneEvolutionary`	One-plus-one evolutionary optimizer configuration

Control Point Registration

`cpselect`	Control Point Selection tool
`fitgeotform2d`	Fit 2-D geometric transformation to control point pairs
`cpcorr`	Tune control point locations using cross-correlation
`cpstruct2pairs`	Extract valid control point pairs from `cpstruct` structure
`imwarp`	Apply geometric transformation to image

Spatial Referencing

`imref2d`	Reference 2-D image to world coordinates
`imref3d`	Reference 3-D image to world coordinates

Topics

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.

Featured Examples

Find Image Rotation and Scale

Use control points to align two images that differ by a rotation and scale change.

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Register Multimodal MRI Images

Align two multimodal MRI images to a common coordinate system using automatic intensity-based image registration.

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Register Multimodal 3-D Medical Images

Align two volumetric images using automatic intensity-based image registration.

Open Live Script

Register Images with Projection Distortion Using Control Points

Register two images by selecting control points common to both images and inferring a geometric transformation that aligns the control points.

Open Live Script

Find Image Rotation and Scale Using Automated Feature Matching

Automatically determine the geometric transformation between a pair of images. When one image is distorted relative to another by rotation and scale, use detectSURFFeatures and estgeotform2d to find the rotation angle and scale factor. You can then transform the distorted image to recover the original image.

Feature Based Panoramic Image Stitching

Automatically create a panorama using feature based image registration techniques.

Video Stabilization Using Point Feature Matching

Stabilize a video that was captured from a jittery platform. One way to stabilize a video is to track a salient feature in the image and use this as an anchor point to cancel out all perturbations relative to it. This procedure, however, must be bootstrapped with knowledge of where such a salient feature lies in the first video frame. In this example, we explore a method of video stabilization that works without any such a priori knowledge. It instead automatically searches for the "background plane" in a video sequence, and uses its observed distortion to correct for camera motion.