Image Registration
Image registration is the process of aligning two or more images. This process involves designating one image as the reference image, also called the fixed image, and applying geometric transformations or local displacements to the other image so that it aligns with the reference image. Images can be misaligned for a variety of reasons. Commonly, images are captured under variable conditions that can change the camera perspective or the content of the scene.
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 Choose an Image Registration Technique.
Apps
| Registration Estimator | Register 2-D grayscale images |
Functions
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.
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. - Register 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. - Select Matching Control Point Pairs
Use the Control Point Selection tool to pick control points in the image to be registered and corresponding points in the fixed image.
Featured Examples
Find Image Rotation and Scale
Use control points to align two images that differ by a rotation and scale change.
Register Multimodal MRI Images
Align two multimodal MRI images to a common coordinate system using automatic intensity-based image registration.
Register Multimodal 3-D Medical Images
Align two volumetric images using automatic intensity-based image registration.
Register Images with Projection Distortion Using Control Points
Register two images by calculating a geometric transformation that aligns control points in the images.
Automatically Find Image Rotation and Scale
Demonstrates how to automatically determine the geometric transformation between two images. Specifically, when one image is distorted in relation to another due to rotation and scaling, the functions detectSURFFeatures and estimateGeometricTransform2D can be employed to identify the rotation angle and scale factor. Subsequently, these parameters can be used to transform the distorted image back to its original appearance.
Create Panorama
Automatically stitch multiple images into panorama. The procedure for image stitching is an extension of feature based image registration. Instead of registering a single pair of images, multiple image pairs are successively registered relative to each other to form a panorama.
Stabilize Video Using Image Point Features
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 the observed distortion to correct for camera motion.
