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영상 정합

명암 상관, 특징 매칭 또는 제어점 매핑을 사용하여 두 개의 영상 정렬

Image Processing Toolbox™와 Computer Vision Toolbox™는 정합 추정기 앱을 사용한 대화형 방식의 정합, 명암 기반의 자동 영상 정합, 제어점 정합 및 자동화된 특징 매칭과 같이 모두 네 가지의 영상 정합 솔루션을 제공합니다. 이러한 접근 방식 중에서 어느 것을 선택할지에 대한 도움을 받으려면 Approaches to Registering Images 항목을 참조하십시오.

정합 추정기Register 2-D grayscale images

함수

모두 확장

imregister명암 기반 영상 정합
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
normxcorr2정규화된 2차원 상호상관
MattesMutualInformationMattes mutual information metric configuration
MeanSquaresMean square error metric configuration
RegularStepGradientDescentRegular step gradient descent optimizer configuration
OnePlusOneEvolutionaryOne-plus-one evolutionary optimizer configuration
cpselect Control Point Selection tool
fitgeotrans제어점 쌍에 기하 변환 피팅
cpcorrTune control point locations using cross-correlation
cpstruct2pairsExtract valid control point pairs from cpstruct structure
imwarp영상에 기하 변환 적용

객체

모두 확장

imref2dReference 2-D image to world coordinates
imref3dReference 3-D image to world coordinates
affine2d2차원 아핀 기하 변환
affine3d 3차원 아핀 기하 변환
projective2d 2-D projective geometric transformation

도움말 항목

대화형 방식으로 영상 정합하기

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 ten algorithms for feature-based, intensity-based, and nonrigid registration.

명암 기반 최적화를 사용하여 영상 정합하기

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.

다중모달 MRI 영상 정합하기

이 예제에서는 자동 명암 기반 영상 정합을 사용하여 두 다중모달 MRI 영상을 단일 공통 좌표 시스템에 맞추는 방법을 보여줍니다.

Register Multimodal 3-D Medical Images

This example shows how to align two volumetric images using automatic intensity-based image registration.

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.

제어점 매핑을 사용하여 영상 정합하기

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.

Find Image Rotation and Scale

This example shows how to use control points to align two images that differ by a rotation and scale change.

Use Cross-Correlation to Improve Control Point Placement

Fine-tune your control point selections using cross-correlation.

Register Images with Projection Distortion Using Control Points

This example shows how to register two images by selecting control points common to both images and inferring a geometric transformation that aligns the control points.