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Random Germs and Stochastic Watershed for Unsupervised Multispectral Image Segmentation
 

Summary: Random Germs and Stochastic Watershed for
Unsupervised Multispectral Image Segmentation
Guillaume Noyel, Jes´us Angulo, and Dominique Jeulin
Centre de Morphologie Math´ematique, Ecole des Mines de Paris
35 rue Saint Honor´e, 77305 Fontainebleau, France
{guillaume.noyel,jesus.angulo,dominique.jeulin}@ensmp.fr
Abstract. This paper extends the use of stochastic watershed, recently
introduced by Angulo and Jeulin [1], to unsupervised segmentation of
multispectral images. Several probability density functions (pdf), derived
from Monte Carlo simulations (M realizations of N random markers),
are used as a gradient for segmentation: a weighted marginal pdf a vec-
torial pdf and a probabilistic gradient. These gradient-like functions are
then segmented by a volume-based watershed algorithm to define the R
largest regions. The various gradients are computed in multispectral im-
age space and in factor image space, which gives the best segmentation.
Results are presented on PLEIADES satellite simulated images.
Keywords: multispectral image, unsupervised segmentation, mathe-
matical morphology, stochastic watershed.
1 Introduction
Watershed transformation is one of the most powerful tools for image segmenta-

  

Source: Angulo,Jesús - Centre de Morphologie Mathématique, Ecole des Mines de Paris

 

Collections: Computer Technologies and Information Sciences