@conference {1274,
	title = {K{\"o}r alak{\'u} objektumok szegment{\'a}l{\'a}sa Markov mez{\H o} seg{\'\i}ts{\'e}g{\'e}vel},
	booktitle = {A K{\'e}pfeldolgoz{\'o}k {\'e}s Alakfelismer{\H o}k T{\'a}rsas{\'a}g{\'a}nak konferenci{\'a}ja - K{\'E}PAF 2009},
	year = {2009},
	note = {Received the Attila Kuba Prize},
	month = {Jan 2009},
	pages = {1 - 9},
	publisher = {Akaprint},
	organization = {Akaprint},
	type = {Conference paper},
	address = {Budapest},
	url = {http://vision.sztaki.hu/~kepaf/kepaf2009_CD/files/116-4-MRFCircle08.pdf}
}
@inbook {1231,
	title = {A Markov random field model for extracting near-circular shapes},
	booktitle = {16th IEEE International Conference on Image Processing (ICIP)},
	year = {2009},
	note = {UT: 000280464300268ScopusID: 77951945383doi: 10.1109/ICIP.2009.5413472},
	month = {Nov 2009},
	pages = {1073 - 1076},
	publisher = {IEEE},
	organization = {IEEE},
	type = {Conference paper},
	address = {Cairo, Egypt},
	abstract = {<p>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 {\textquoteright}gas of circles{\textquoteright} phase field model in a principled way, thereby creating an {\textquoteright}equivalent{\textquoteright}MRF. The behaviour of the resultingMRF 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. {\textcopyright}2009 IEEE.</p>},
	isbn = {978-1-4244-5653-6 },
	doi = {10.1109/ICIP.2009.5413472 },
	author = {Tam{\'a}s Blaskovics and Zoltan Kato and Ian Jermyn}
}
