Fast marching method: Difference between revisions

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: <math>|\nabla u(x)|=1/f(x) \text{ for } x \in \Omega</math>

: <math>u(x) = 0 \text{ for } x \in \partial \Omega</math>

Typically, such a problem describes the evolution of a closed surface as a function of time <math>u</math> with speed <math>f</math> in the normal direction at a point <math>x</math> on the propagating surface. The speed function is specified, and the time at which the contour crosses a point <math>x</math> is obtained by solving the equation. Alternatively, <math>u(x)</math> can be thought of as the minimum amount of time it would take to reach <math>\partial\Omega</math> starting from the point <math>x</math>. The fast marching method takes advantage of this [[optimal control]] interpretation of the problem in order to build a solution outwards starting from the "known information", i.e. the boundary values.

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==Algorithm==

First, assume that the ___domain has been discretized into a mesh. We will refer to meshpointsmesh points as nodes. Each node <math>x_i</math> has a corresponding value <math>U_i = U(x_i) \approx u(x_i)</math>.

The algorithm works just like Dijkstra's algorithm but differs in how the nodes' values are calculated. In Dijkstra's algorithm, a node's value is calculated using a single one of the neighboring nodes. However, in solving the [[Partial differential equation|PDE]] in <math>\mathbb{R}^n</math>, between <math>1</math> and <math>n</math> of the neighboring nodes [[Eikonal equation#Numerical Approximation|are used]].

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* [https://rd.springer.com/article/10.1007/s11075-008-9183-x Generalized Fast Marching method] by Forcadel et al. [2008] for applications in image segmentation.

* [https://github.com/scikit-fmm/scikit-fmm Python Implementation of the Fast Marching Method]

*See Chapter 8 in [http://etd.fcla.edu/CF/CFE0001159/Rumpf_Raymond_C_200608_PhD.pdf Design and Optimization of Nano-Optical Elements by Coupling Fabrication to Optical Behavior] {{Webarchive|url=https://web.archive.org/web/20130820063106/http://etd.fcla.edu/CF/CFE0001159/Rumpf_Raymond_C_200608_PhD.pdf |date=2013-08-20 }}

==Notes==