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DGCI
2009
Springer

Multivariate Watershed Segmentation of Compositional Data

10 years 11 months ago
Multivariate Watershed Segmentation of Compositional Data
Abstract. Watershed segmentation of spectral images is typically achieved by first transforming the high-dimensional input data into a scalar boundary indicator map which is used to derive the watersheds. We propose to combine a Random Forest classifier with the watershed transform and introduce three novel methods to obtain scalar boundary indicator maps from class probability maps. We further introduce the multivariate watershed as a generalization of the classic watershed approach.
Michael Hanselmann, Ullrich Köthe, Bernhard Y
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where DGCI
Authors Michael Hanselmann, Ullrich Köthe, Bernhard Y. Renard, Marc Kirchner, Ron M. A. Heeren, Fred A. Hamprecht
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