Segmentation of curvilinear objects using a watershed-based curve adjacency graph

From LRDE

Revision as of 17:57, 4 January 2018 by Bot (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)

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.ibpria,
  author	= {Thierry G\'eraud},
  title		= {Segmentation of curvilinear objects using a
		  watershed-based curve adjacency graph},
  booktitle	= {Proceedings of the 1st Iberian Conference on Pattern
		  Recognition and Image Analysis (IbPRIA)},
  pages		= {279--286},
  year		= 2003,
  editor	= {Springer-Verlag},
  volume	= 2652,
  series	= {Lecture Notes in Computer Science Series},
  address	= {Mallorca, Spain},
  month		= jun,
  publisher	= {Springer-Verlag},
  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.}
}