图像配准
图像配准是对齐两个或多个图像的过程。此过程涉及将一个图像指定为参考图像，亦称固定图像，并对另一个图像应用几何变换或局部位移，以使其与参考图像对齐。图像可能会因为各种原因而未对齐。通常，图像是在可变条件下捕获的，这些条件会改变相机视角或场景内容。
Image Processing Toolbox™ 和 Computer Vision Toolbox™ 一共提供四种图像配准解决方案：使用图像配准器的交互式配准、基于强度的自动图像配准、控制点配准和自动特征匹配。有关选择使用哪种方法的帮助，请参阅Choose an Image Registration Technique。
App
| 图像配准器 | 配准二维灰度图像 |
函数
主题
以交互方式配准图像
- Register Images Using Registration Estimator App
Align a pair of images using feature-based registration techniques in the Registration Estimator app.
使用基于强度的优化配准图像
- 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. - 为基于强度的图像配准创建优化器和度量
选择适用于单模态或多模态图像的图像度量和优化器。 - Use Correlation as Preprocessing Step in Image Registration
Use correlation to estimate an initial geometric 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.
使用控制点映射配准图像
- 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.
精选示例
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.
