Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Stochastic watershed segmentation Jess Angulo and Dominique Jeulin
 

Summary: Stochastic watershed segmentation
Jesús Angulo and Dominique Jeulin
Centre de Morphologie Mathématique, Ecole des Mines de Paris, 35, rue Saint-Honoré,
77305 Fontainebleau, France
jesus.angulo,dominique.jeulin@ensmp.fr
Abstract This paper introduces a watershed-based stochastic segmentation
methodology. The approach is based on using M realizations of
N random markers to build a probability density function (pdf) of
contours which is then segmented by volumic watershed for den-
ing the R most signicant regions. It over-performs the standard
watershed algorithms when the aim is to segment complex images
into a few regions. Three variants of the random germs framework
are discussed, according to the algorithm used to build the pdf: 1)
uniform random germs on the same gradient, 2) regionalised ran-
dom germs on the same gradient, and 3) uniform random germs on
levelled-based gradient. The last algorithm is more complex but it
yields the best results.
Keywords: watershed transformation, leveling, Poisson points, density of con-
tours, random germs segmentation.
1. Introduction

  

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

 

Collections: Computer Technologies and Information Sciences