J. Weickert,
Fast segmentation methods based on partial differential
equations and the watershed transformation,
P. Levi, R.-J. Ahlers, F. May, M. Schanz (Eds.),
Mustererkennung 1998, Springer, Berlin, 93-100, 1998.
Segmentation algorithms are presented
which combine regularization by nonlinear partial differential
equations (PDEs) with a watershed transformation with region
merging.
We develop efficient algorithms for two well-founded PDE methods.
They use an additive operator splitting (AOS) leading to recursive
and separable filters. Further speed-up can be obtained by embedding
AOS schemes into a pyramid framework.
Examples demonstrate that the preprocessing by
these PDE techniques eases and stabilizes the segmentation. The
typical CPU time for segmenting a 256^2 image on a workstation is
less than 2 seconds.
The full paper is available
online as well.
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