Image registration
Image registration is the process of transforming different sets of data into one coordinate system.
Non-parametric models on the other hand, do not follow any parameterization, allowing each image element to be displaced arbitrarily.
For this reason, flows which generalize the ideas of additive groups allow for generating large deformations that preserve topology, providing 1-1 and onto transformations.
There are a number of programs which generate diffeomorphic transformations of coordinates via diffeomorphic mapping including MRI Studio[11] and MRI Cloud.org[12] Spatial methods operate in the image domain, matching intensity patterns or features in images.
Unlike many spatial-domain algorithms, the phase correlation method is resilient to noise, occlusions, and other defects typical of medical or satellite images.
Additionally, the phase correlation uses the fast Fourier transform to compute the cross-correlation between the two images, generally resulting in large performance gains.
[13][14] Due to properties of the Fourier transform, the rotation and scaling parameters can be determined in a manner invariant to translation.
A confident registration with a measure of uncertainty is critical for many change detection applications such as medical diagnostics.
Several notable papers have attempted to quantify uncertainty in image registration in order to compare results.
[16][17] However, many approaches to quantifying uncertainty or estimating deformations are computationally intensive or are only applicable to limited sets of spatial transformations.
Image registration has applications in remote sensing (cartography updating), and computer vision.
Due to the vast range of applications to which image registration can be applied, it is impossible to develop a general method that is optimized for all uses.
Medical image registration (for data of the same patient taken at different points in time such as change detection or tumor monitoring) often additionally involves elastic (also known as nonrigid) registration to cope with deformation of the subject (due to breathing, anatomical changes, and so forth).
In astrophotography, image alignment and stacking are often used to increase the signal to noise ratio for faint objects.
There are many different techniques that can be implemented in real time and run on embedded devices like cameras and camera-phones.
