Multivariate Watershed Segmentation of Compositional Data

被引：0

作者：

Hanselmann, Michael ^{[1
]}

Koethe, Ullrich ^{[1
]}

Renard, Bernhard Y. ^{[1
]}

Kirchner, Marc ^{[1
]}

Heeren, Ron M. A. ^{[2
]}

Hamprecht, Fred A. ^{[1
]}

机构：

[1] Heidelberg Univ, Heidelberg Collaboratory Image Proc, D-6900 Heidelberg, Germany

[2] FOM Inst Atom & Mol Phys, Amsterdam, Netherlands

来源：

DISCRETE GEOMETRY FOR COMPUTER IMAGERY, PROCEEDINGS | 2009年 / 5810卷

关键词：

CLASSIFICATION; ALGORITHM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：180 / +

页数：3

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