by Tamas Blaskovics, Zoltan Kato, Ian Jermyn
Abstract:
We propose a binary Markov Random Field (MRF) model that assigns high probability to regions in the image domain consisting of an unknown number of circles of a given radius. We construct the model by discretizing the 'gas of circles' phase field model in a principled way, thereby creating an 'equivalent' MRF. The behaviour of the resulting MRF model is analyzed, and the performance of the new model is demonstrated on various synthetic images as well as on the problem of tree crown detection in aerial images.
Reference:
Tamas Blaskovics, Zoltan Kato, Ian Jermyn, A Markov Random Field Model for Extracting Near-Circular Shapes, In Proceedings of International Conference on Image Processing, Cairo, Egypt, pp. 1073-1076, 2009, IEEE.
Bibtex Entry:
@string{icip="Proceedings of International Conference on Image Processing"}
@InProceedings{Blaskovics-etal2009a,
author = {Tamas Blaskovics and Kato, Zoltan and Ian Jermyn},
title = {A {M}arkov Random Field Model for Extracting
Near-Circular Shapes},
booktitle = icip,
pages = {1073--1076},
year = 2009,
address = {Cairo, Egypt},
month = nov,
organization = {IEEE},
publisher = {IEEE},
abstract = {We propose a binary Markov Random Field (MRF) model
that assigns high probability to regions in the
image domain consisting of an unknown number of
circles of a given radius. We construct the model by
discretizing the 'gas of circles' phase
field model in a principled way, thereby creating an
'equivalent' MRF. The behaviour of the
resulting MRF model is analyzed, and the performance
of the new model is demonstrated on various
synthetic images as well as on the problem of tree
crown detection in aerial images.},
pdf = {papers/icip2009.pdf}
}