PROCEEDINGS IPMU '08
Image segmentation using A-IFSs
P. Couto, M. Pagola, H. Bustince, E. Barrenechea, P. Melo-Pinto.
The problem of segmentation in spite
of all the work over the last decades, is
still an important research field and also
a critical preprocessing step for image
processing, mostly due to the fact that
finding a global optimal threshold that
works well for all kind of images is indeed
a near impossible task that, probably,
will never be accomplished.
During the past years, fuzzy logic theory
has been successfully applied to image
thresholding. Moreover, considering
that for segmentation purposes, in
most cases, image pixels have an inherent
ambiguity in the predicate that
they must fulfill in order to belong to
an object, which results in the experts
uncertainty in assigning the pixel to
that object. In the context of fuzzy
sets theory, Atanassov’s Intuitionistic
fuzzy sets are a relevant and interesting
extension since, uncertainty is one
of the underlying ideas behind this theory.
In this paper we describe a thresholding
technique using Atanassov’s intuitionistic
fuzzy sets (A-IFSs). This
approach uses Atanassov’s intuitionistic
index values for representing the uncertainty
of the expert in determining
that the pixel belongs to the background
or that it belongs to the object. We first
introduce the general framework of this
approach and then its natural extension
to multilevel thresholding.
Segmentation experimental results and
their performance evaluation for the
calculation of one, two and three
thresholds are presented.
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