Image registration is the process of aligning two or more images of the same scene. This process involves designating one image as the reference (also called the reference image or the fixed image), and applying geometric transformations to the other images so that they align with the reference. Images can be misaligned for a variety of reasons. Commonly, the images are captured under variable conditions that can change camera perspective. Misalignment can also be the result of lens and sensor distortions or differences between capture devices.
A geometric transformation maps locations in one image to new locations in another image. The step of determining the correct geometric transformation parameters is key to the image registration process.
Image registration is often used as a preliminary step in other image processing applications. For example, you can use image registration to align satellite images or to align medical images captured with different diagnostic modalities (MRI and SPECT). Image registration allows you to compare common features in different images. For example, you might discover how a river has migrated, how an area became flooded, or whether a tumor is visible in an MRI or SPECT image.
Together, the Image Processing Toolbox™ and Computer Vision System Toolbox™ offer three image registration solutions:
Intensity-Based Automatic Image Registration maps certain pixels in each image to the same location based on relative intensity patterns. This approach is best suited for workflows that involve a large collection of images or when you require an automated workflow. This functionality resides in the Image Processing Toolbox.
Control Point Registration allows you to manually select common features in each image to map to the same pixel location. This method of registration is best suited for images that have distinct features. It resides in the Image Processing Toolbox.
An automated feature-based workflow automatically aligns images by selecting matching features between two images. This workflow includes feature detection, extraction, and matching, followed by transform estimation. Features can be corners or blobs and the distortion can include rotation and scale changes. For more information, see Finding Rotation and Scale of an Image Using Automated Feature Matching. You must have the Computer Vision System Toolbox installed to use this method.