Difference between revisions of "Publications/bloch.05.prl"

From LRDE

 
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| type = article
 
| type = article
 
| id = bloch.05.prl
 
| id = bloch.05.prl
  +
| identifier = doi:10.1016/j.patrec.2004.08.009
 
| bibtex =
 
| bibtex =
 
@Article<nowiki>{</nowiki> bloch.05.prl,
 
@Article<nowiki>{</nowiki> bloch.05.prl,
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month = mar,
 
month = mar,
 
pages = <nowiki>{</nowiki>449--457<nowiki>}</nowiki>,
 
pages = <nowiki>{</nowiki>449--457<nowiki>}</nowiki>,
  +
doi = <nowiki>{</nowiki>10.1016/j.patrec.2004.08.009<nowiki>}</nowiki>,
 
abstract = <nowiki>{</nowiki>Spatial relations play an important role in recognition of
 
abstract = <nowiki>{</nowiki>Spatial relations play an important role in recognition of
 
structures embedded in a complex environment and for
 
structures embedded in a complex environment and for

Latest revision as of 12:46, 24 November 2020

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.


Bibtex (lrde.bib)

@Article{	  bloch.05.prl,
  author	= {Isabelle Bloch and Olivier Colliot and Oscar Camara and
		  Thierry G\'eraud},
  title		= {Fusion of spatial relationships for guiding recognition,
		  example of brain structure recognition in {3D} {MRI}},
  journal	= {Pattern Recognition Letters},
  year		= 2005,
  volume	= 26,
  number	= 4,
  month		= mar,
  pages		= {449--457},
  doi		= {10.1016/j.patrec.2004.08.009},
  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.}
}