Signed distance function

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A disk (top) and its signed distance function (bottom, in red). The x-y plane in shown in blue.
A disk (top) and its signed distance function (bottom, in red). The x-y plane in shown in blue.
A more complicated set (top) and its signed distance function (bottom, in red).
A more complicated set (top) and its signed distance function (bottom, in red).

In mathematics and applications, the signed distance function of a set S in a metric space determines how close a given point x is to the boundary of S, with that function having positive values at points x inside S, it decreases in value as x approaches the boundary of S where the signed distance function is zero, and it takes negative values outside of S.

Formally, if (X, d) is a metric space, the signed distance function f is defined by

f(x)=  \begin{cases}  d(x, \partial S) & \mbox{ if } x\in S \\  -d(x, \partial S)&  \mbox{ if } x\not\in S \end{cases}

where

d(x, \partial S)=\inf_{y\in\partial S}d(x, y)

(the ∂ symbol denotes the set boundary, while 'inf' is the infimum).

If S is a subset of the Euclidean space Rn with piecewise smooth boundary, the signed distance function is differentiable almost everywhere, and its gradient satisfies the eikonal equation

|\nabla f|=1.

An efficient algorithm for calculating the signed distance function is the fast marching method by James Sethian.

Signed distance functions are applied for example in computer vision.

[edit] See also

[edit] References

  • J.A. Sethian, Level set methods and fast marching methods. Cambridge University Press (1999). ISBN 0-521-64557-3.
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