Fast color image segmentation based on levellings in feature Space

Authors

Thierry Géraud, Giovanni Palma, Niels Van Vliet

Where

Computer Vision and Graphics---International Conference on Computer Vision and Graphics (ICCVG), Warsaw, PolandSeptember 2004

Type

inproceedings

Projects

Olena

Date

2004-08-11

Abstract

This paper presents a morphological classifier with application to color image segmentation. The basic idea of a morphological classifier is to consider that a color histogram is a 3D gray-level image and that morphological operators can be applied to modify this image. The final objective is to extract clusters in color space, that isidentify regions in the 3D image. In this paper, we particularly focus on a powerful class of morphology-based filters called levellings to transform the 3D histogram-image to identify clusters. We also show that our method gives better results than the ones of state-of-the-art methods.

Documents

• Slides

Bibtex (lrde.bib)

@InProceedings{	  geraud.04.iccvg,
  author	= {Thierry G\'eraud and Giovanni Palma and Niels {Van Vliet}},
  title		= {Fast color image segmentation based on levellings in
		  feature Space},
  booktitle	= {Computer Vision and Graphics---International Conference on
		  Computer Vision and Graphics (ICCVG), Warsaw, Poland,
		  September 2004},
  year		= 2004,
  series	= {Computational Imaging and Vision},
  volume	= 32,
  editor	= {Kluwer Academic Publishers},
  pages		= {800--807},
  note		= {On CD.},
  abstract	= {This paper presents a morphological classifier with
		  application to color image segmentation. The basic idea of
		  a morphological classifier is to consider that a color
		  histogram is a 3D gray-level image and that morphological
		  operators can be applied to modify this image. The final
		  objective is to extract clusters in color space, that is,
		  identify regions in the 3D image. In this paper, we
		  particularly focus on a powerful class of morphology-based
		  filters called levellings to transform the 3D
		  histogram-image to identify clusters. We also show that our
		  method gives better results than the ones of
		  state-of-the-art methods.}
}