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Registro de imagen

Alinee dos imágenes mediante técnicas de correlación de intensidades, coincidencia de rasgos o correspondencia de puntos de control

En conjunto, Image Processing Toolbox™ y Computer Vision Toolbox™ ofrecen cuatro soluciones de registro de imagen: registro interactivo con la app Registration Estimator, registro automático basado en la intensidad, registro basado en puntos de control y coincidencia automática de características. Si necesita ayuda para seleccionar un enfoque, consulte Approaches to Registering Images.

Apps

Registration Estimator

Funciones

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Registro automático

`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`	Correlación cruzada 2D normalizada
`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

Registro basado en puntos de control

`cpselect`	Control Point Selection tool
`fitgeotrans`	Fit 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

Referencias espaciales

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

Temas

Registro interactivo de imágenes

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.

Registrar imágenes con la optimización basada en intensidades

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.

Registrar imágenes con la correspondencia entre puntos de control

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.

Ejemplos destacados

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.

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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.

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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

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.