Local reasoning in fuzzy attribute graphs for optimizing sequential segmentation

From LRDE

Revision as of 17:56, 4 January 2018 by Bot (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)

Abstract

Spatial relations play a crucial role in model-based image recognition and interpretation due to their stability compared to many other image appearance characteristics. Graphs are well adapted to represent such information. Sequential methods for knowledge-based recognition of structures require to define in which order the structures have to be recognized. We propose to address this problem of order definition by developing algorithms that automatically deduce sequential segmentation paths from fuzzy spatial attribute graphs. As an illustration, these algorithms are applied on brain image understanding.


Bibtex (lrde.bib)

@InProceedings{	  fouquier.07.gbr,
  author	= {Geoffroy Fouquier and Jamal Atif and Isabelle Bloch},
  title		= {Local reasoning in fuzzy attribute graphs for optimizing
		  sequential segmentation},
  booktitle	= {Proceedings of the 6th IAPR TC-15 Workshop on Graph-based
		  Representations in Pattern Recognition (GBR)},
  year		= 2007,
  month		= jun,
  address	= {Alicante, Spain},
  volume	= {LNCS 4538},
  editor	= {F. Escolano and M. Vento},
  publisher	= {Springer Verlag},
  isbn		= {978-3-540-72902-0},
  pages		= {138--147},
  abstract	= {Spatial relations play a crucial role in model-based image
		  recognition and interpretation due to their stability
		  compared to many other image appearance characteristics.
		  Graphs are well adapted to represent such information.
		  Sequential methods for knowledge-based recognition of
		  structures require to define in which order the structures
		  have to be recognized. We propose to address this problem
		  of order definition by developing algorithms that
		  automatically deduce sequential segmentation paths from
		  fuzzy spatial attribute graphs. As an illustration, these
		  algorithms are applied on brain image understanding.}
}