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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
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