The notion of orbit of a control system used in mathematical control theory is a particular case of the notion of orbit in group theory. [1] [2] [3]
Let be a control system, where belongs to a finite-dimensional manifold and belongs to a control set . Consider the family and assume that every vector field in is complete. For every and every real , denote by the flow of at time .
The orbit of the control system through a point is the subset of defined by
The difference between orbits and attainable sets is that, whereas for attainable sets only forward-in-time motions are allowed, both forward and backward motions are permitted for orbits. In particular, if the family is symmetric (i.e., if and only if ), then orbits and attainable sets coincide.
The hypothesis that every vector field of is complete simplifies the notations but can be dropped. In this case one has to replace flows of vector fields by local versions of them.
Each orbit is an immersed submanifold of .
The tangent space to the orbit at a point is the linear subspace of spanned by the vectors where denotes the pushforward of by , belongs to and is a diffeomorphism of of the form with and .
If all the vector fields of the family are analytic, then where is the evaluation at of the Lie algebra generated by with respect to the Lie bracket of vector fields. Otherwise, the inclusion holds true.
If for every and if is connected, then each orbit is equal to the whole manifold .
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Computational anatomy is an interdisciplinary field of biology focused on quantitative investigation and modelling of anatomical shapes variability. It involves the development and application of mathematical, statistical and data-analytical methods for modelling and simulation of biological structures.
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Computational anatomy (CA) is a discipline within medical imaging focusing on the study of anatomical shape and form at the visible or gross anatomical scale of morphology. The field is broadly defined and includes foundations in anatomy, applied mathematics and pure mathematics, including medical imaging, neuroscience, physics, probability, and statistics. It focuses on the anatomical structures being imaged, rather than the medical imaging devices. The central focus of the sub-field of computational anatomy within medical imaging is mapping information across anatomical coordinate systems most often dense information measured within a magnetic resonance image (MRI). The introduction of flows into CA, which are akin to the equations of motion used in fluid dynamics, exploit the notion that dense coordinates in image analysis follow the Lagrangian and Eulerian equations of motion. In models based on Lagrangian and Eulerian flows of diffeomorphisms, the constraint is associated to topological properties, such as open sets being preserved, coordinates not crossing implying uniqueness and existence of the inverse mapping, and connected sets remaining connected. The use of diffeomorphic methods grew quickly to dominate the field of mapping methods post Christensen's original paper, with fast and symmetric methods becoming available.
Diffeomorphometry is the metric study of imagery, shape and form in the discipline of computational anatomy (CA) in medical imaging. The study of images in computational anatomy rely on high-dimensional diffeomorphism groups which generate orbits of the form , in which images can be dense scalar magnetic resonance or computed axial tomography images. For deformable shapes these are the collection of manifolds , points, curves and surfaces. The diffeomorphisms move the images and shapes through the orbit according to which are defined as the group actions of computational anatomy.