Fast Segmentation Methods Based on Partial Differential Equations and the Watershed Transformation
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 2562 image on a workstation is less than 2 seconds.
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