Difference between revisions of "Publications/geraud.03.icisp"

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Line 6: Line 6:
 
| booktitle = Proceedings of the International Conference on Image and Signal Processing (ICISP)
 
| booktitle = Proceedings of the International Conference on Image and Signal Processing (ICISP)
 
| volume = 2
 
| volume = 2
| pages = 404–411
+
| pages = 404 to 411
 
| address = Agadir, Morocco
 
| address = Agadir, Morocco
 
| publisher = Faculty of Sciences at Ibn Zohr University, Morocco
 
| publisher = Faculty of Sciences at Ibn Zohr University, Morocco

Latest revision as of 17:57, 4 January 2018

Abstract

This paper presents a general framework to segment curvilinear objects in 2D images. A pre-processing step relies on mathematical morphology to obtain a connected line which encloses curvilinear objects. Then, a graph is constructed from this line and a Markovian Random Field is defined to perform objects segmentation. Applications of our framework are numerous: they go from simple surve segmentation to complex road network extraction in satellite images.


Bibtex (lrde.bib)

@InProceedings{	  geraud.03.icisp,
  author	= {Thierry G\'eraud},
  title		= {Segmentation d'objets curvilignes \`a l'aide des champs de
		  Markov sur un graphe d'adjacence de courbes issu de
		  l'algorithme de la ligne de partage des eaux},
  booktitle	= {Proceedings of the International Conference on Image and
		  Signal Processing (ICISP)},
  year		= 2003,
  volume	= 2,
  pages		= {404--411},
  address	= {Agadir, Morocco},
  month		= jun,
  publisher	= {Faculty of Sciences at Ibn Zohr University, Morocco},
  note		= {In French},
  abstract	= {This paper presents a general framework to segment
		  curvilinear objects in 2D images. A pre-processing step
		  relies on mathematical morphology to obtain a connected
		  line which encloses curvilinear objects. Then, a graph is
		  constructed from this line and a Markovian Random Field is
		  defined to perform objects segmentation. Applications of
		  our framework are numerous: they go from simple surve
		  segmentation to complex road network extraction in
		  satellite images.}
}