by Etienne Bertin, Horst Bischof
Abstract:
We present an algorithm for image segmentation with irregular pyramids. Instead of starting with the original pixel grid, we first apply some adaptive Voronoi tesselation to the image. This provides the advantage that the number of cells in the bottom level of the pyramid is already reduced as compared to the number of pixels of the original image. Furthermore the Voronoi diagram is a powerful tool for shape description and image compression. For the construction of the irregular pyramid we present a Hopfield neural network which controls the decimation process. In this paper we extend our previous results by proving a more general theorem. The contributions of this paper are the initialisation of the pyramid by a Delaunay graph and the extension of the results for Hopfield neural networks for decimation. The validity of our approach is demonstrated by several examples.
Reference:
Voronoi Pyramids controlled by Hopfield Neural Networks (Etienne Bertin, Horst Bischof), Technical report, PRIP, TU Wien, 1993.
Bibtex Entry:
@TechReport{TR024,
author = "Etienne Bertin and Horst Bischof",
institution = "PRIP, TU Wien",
number = "PRIP-TR-024",
title = "Voronoi {P}yramids controlled by {H}opfield {N}eural
{N}etworks",
year = "1993",
url = "https://www.prip.tuwien.ac.at/pripfiles/trs/tr24.pdf",
abstract = "We present an algorithm for image segmentation with
irregular pyramids. Instead of starting with the
original pixel grid, we first apply some adaptive
Voronoi tesselation to the image. This provides the
advantage that the number of cells in the bottom
level of the pyramid is already reduced as compared
to the number of pixels of the original
image. Furthermore the Voronoi diagram is a powerful
tool for shape description and image
compression. For the construction of the irregular
pyramid we present a Hopfield neural network which
controls the decimation process. In this paper we
extend our previous results by proving a more
general theorem. The contributions of this paper are
the initialisation of the pyramid by a Delaunay
graph and the extension of the results for Hopfield
neural networks for decimation. The validity of our
approach is demonstrated by several examples.",
}