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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 EstimatorRegister 2-D grayscale images

Functions

expand all

imregisterIntensity-based image registration
imregconfigConfigurations for intensity-based registration
imregtform Estimate geometric transformation that aligns two 2-D or 3-D images
imregcorrEstimate geometric transformation that aligns two 2-D images using phase correlation
imregdemonsEstimate displacement field that aligns two 2-D or 3-D images
imregmtbRegister 2-D images using median threshold bitmaps
normxcorr2Normalized 2-D cross-correlation
MattesMutualInformationMattes mutual information metric configuration
MeanSquaresMean square error metric configuration
RegularStepGradientDescentRegular step gradient descent optimizer configuration
OnePlusOneEvolutionaryOne-plus-one evolutionary optimizer configuration
cpselectControl Point Selection tool
fitgeotransFit geometric transformation to control point pairs
cpcorrTune control point locations using cross-correlation
cpstruct2pairsExtract valid control point pairs from cpstruct structure
imwarpApply geometric transformation to image
imref2dReference 2-D image to world coordinates
imref3dReference 3-D image to world coordinates

Topics

Register Images Interactively

Register Images Using Registration Estimator App

This example shows how to align a pair of images using the Registration Estimator app.

Techniques Supported by Registration Estimator App

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

Phase correlation is useful to estimate an initial transformation when images are severely misaligned.

Registering an Image Using Normalized Cross-Correlation

This example shows how to 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 transformation, you can pick corresponding points in a pair of images.

Geometric Transformation Types for Control Point Registration

Control point registration can infer the parameters for nonreflective 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

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

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

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 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

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 estimateGeometricTransform2D 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

Feature Based Panoramic Image Stitching

Automatically create a panorama using feature based image registration techniques.

Video Stabilization Using Point Feature Matching

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.

Image Processing Toolbox Documentation

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