Spatial relations play an important role in recognition of structures
embedded in a complex environment and for reasoning under imprecision.
Several types of relationships can be modeled in a unified way using
fuzzy mathematical morphology. Their combination benefits from the
powerful framework of fuzzy set theory for fusion tasks and decision
making. This paper presents several methods of fusion of information
about spatial relationships and illustrates them on the example of
model-based recognition of brain structures in 3D magnetic resonance
imaging.
%0 Journal Article
%1 Bloch2005
%A Bloch, Isabelle
%A Colliot, Olivier
%A Camara, Oscar
%A G�raud, Thierry
%B ICAPR 2003
%D 2005
%J Pattern Recognition Letters
%K Brain Fuzzy Information Knowledge Model-based Spatial fusion, imaging mathematical morphology, recognition, relationships, representation,
%N 4
%P 449--457
%T Fusion of spatial relationships for guiding recognition, example
of brain structure recognition in 3D MRI
%U http://www.sciencedirect.com/science/article/B6V15-4D9R99D-1/1/a15bd06074072fe779d41aafe7b89003
%V 26
%X Spatial relations play an important role in recognition of structures
embedded in a complex environment and for reasoning under imprecision.
Several types of relationships can be modeled in a unified way using
fuzzy mathematical morphology. Their combination benefits from the
powerful framework of fuzzy set theory for fusion tasks and decision
making. This paper presents several methods of fusion of information
about spatial relationships and illustrates them on the example of
model-based recognition of brain structures in 3D magnetic resonance
imaging.
@article{Bloch2005,
abstract = {Spatial relations play an important role in recognition of structures
embedded in a complex environment and for reasoning under imprecision.
Several types of relationships can be modeled in a unified way using
fuzzy mathematical morphology. Their combination benefits from the
powerful framework of fuzzy set theory for fusion tasks and decision
making. This paper presents several methods of fusion of information
about spatial relationships and illustrates them on the example of
model-based recognition of brain structures in 3D magnetic resonance
imaging.},
added-at = {2009-09-12T19:19:34.000+0200},
author = {Bloch, Isabelle and Colliot, Olivier and Camara, Oscar and G�raud, Thierry},
biburl = {https://www.bibsonomy.org/bibtex/29a3fb4e7533f658ca88b00fc2eb5b8c1/mozaher},
booktitle = {ICAPR 2003},
file = {:Bloch2005.pdf:PDF},
interhash = {34eca95c04d577273438fec585e8b2b7},
intrahash = {9a3fb4e7533f658ca88b00fc2eb5b8c1},
journal = {Pattern Recognition Letters},
keywords = {Brain Fuzzy Information Knowledge Model-based Spatial fusion, imaging mathematical morphology, recognition, relationships, representation,},
month = {March},
number = 4,
owner = {Mozaher},
pages = {449--457},
timestamp = {2009-09-12T19:19:37.000+0200},
title = {Fusion of spatial relationships for guiding recognition, example
of brain structure recognition in 3D MRI},
url = {http://www.sciencedirect.com/science/article/B6V15-4D9R99D-1/1/a15bd06074072fe779d41aafe7b89003},
volume = 26,
year = 2005
}