Image registration

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Image registration is the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, times, depths, or viewpoints. It is used in computer vision, medical imaging, military automatic target recognition, and compiling and analyzing images and data from satellites.

Overview

Image registration is necessary in order to be able to compare or integrate the data obtained from these different measurements. It involves aligning two or more images of the same scene taken at different times, from different viewpoints, or by different sensors. The aim is to geometrically align the reference image and the sensed image.

Methods

There are several methods for image registration, which can be broadly categorized into:

• Feature-based methods: These methods rely on the detection of features such as points, lines, and contours in the images. Common techniques include the use of scale-invariant feature transform (SIFT), speeded up robust features (SURF), and Harris corner detector.
• Intensity-based methods: These methods use the intensity values of the pixels directly. Techniques include mutual information, cross-correlation, and least squares.
• Transform-based methods: These methods involve the use of mathematical transformations to align the images. Common transformations include affine, projective, and polynomial transformations.

Applications

Image registration has a wide range of applications, including:

• Medical imaging: Aligning images from different modalities such as MRI, CT scan, and ultrasound to provide a comprehensive view of the patient's anatomy.
• Remote sensing: Aligning satellite images taken at different times to monitor changes in the environment.
• Computer vision: Aligning images for object recognition, tracking, and 3D reconstruction.
• Military: Aligning images for automatic target recognition and surveillance.

Challenges

Some of the challenges in image registration include:

• Noise: Images may contain noise that can affect the accuracy of the registration.
• Occlusions: Parts of the image may be occluded, making it difficult to find corresponding features.
• Deformations: Objects in the images may undergo deformations, making it difficult to align them accurately.
• Computational complexity: Some registration methods can be computationally intensive, making them unsuitable for real-time applications.

Related Pages

• Computer vision
• Medical imaging
• Remote sensing
• Image processing
• Pattern recognition