by Peter Horvath, Ian Jermyn, Zoltan Kato, Josiane Zerubia
Abstract:
We present a model of a 'gas of circles', the ensemble of regions in the image domain consisting of an unknown number of circles with approximately fixed radius and short range repulsive interactions, and apply it to the extraction of tree crowns from aerial images. The method uses the recently introduced 'higher order active contours' (HOACs), which incorporate long-range interactions between contour points, and thereby include prior geometric information without using a template shape. This makes them ideal when looking for multiple instances of an entity in an image. We study an existing HOAC model for networks, and show via a stability calculation that circles stable to perturbations are possible for constrained parameter sets. Combining this prior energy with a data term, we show results on aerial imagery that demonstrate the effectiveness of the method and the need for prior geometric knowledge. The model has many other potential applications.
Reference:
Peter Horvath, Ian Jermyn, Zoltan Kato, Josiane Zerubia, A Higher-Order Active Contour Model for Tree Detection, In Proceedings of International Conference on Pattern Recognition, volume 2, Hong Kong, China, pp. 130-133, 2006, IEEE.
Bibtex Entry:
@string{icpr="Proceedings of International Conference on Pattern Recognition"}
@InProceedings{Horvath-etal2006,
author = {Peter Horvath and Ian Jermyn and Zoltan Kato and
Josiane Zerubia},
title = {A Higher-Order Active Contour Model for Tree
Detection},
booktitle = icpr,
pages = {130--133},
year = 2006,
volume = 2,
address = {Hong Kong, China},
month = aug,
organization = {IAPR},
publisher = {IEEE},
pdf = {papers/icpr2006.pdf},
abstract = {We present a model of a 'gas of circles', the
ensemble of regions in the image domain consisting
of an unknown number of circles with approximately
fixed radius and short range repulsive interactions,
and apply it to the extraction of tree crowns from
aerial images. The method uses the recently
introduced 'higher order active contours' (HOACs),
which incorporate long-range interactions between
contour points, and thereby include prior geometric
information without using a template shape. This
makes them ideal when looking for multiple instances
of an entity in an image. We study an existing HOAC
model for networks, and show via a stability
calculation that circles stable to perturbations
are possible for constrained parameter
sets. Combining this prior energy with a data term,
we show results on aerial imagery that demonstrate
the effectiveness of the method and the need for
prior geometric knowledge. The model has many other
potential applications. }
}