@conference {1389,
	title = {2D {\'e}s 3D bin{\'a}ris objektumok line{\'a}ris deform{\'a}ci{\'o}-becsl{\'e}s{\'e}nek numerikus megold{\'a}si lehet{\H o}s{\'e}gei},
	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 2013},
	year = {2013},
	month = {Jan 2013},
	pages = {526 - 541},
	publisher = {NJSZT-K{\'E}PAF},
	organization = {NJSZT-K{\'E}PAF},
	type = {Conference paper},
	address = {Veszpr{\'e}m},
	author = {Attila Tanacs and Joakim Lindblad and Nata{\v s}a Sladoje and Zoltan Kato},
	editor = {L{\'a}szl{\'o} Cz{\'u}ni}
}
@conference {1222,
	title = {3D objektumok line{\'a}ris deform{\'a}ci{\'o}inak becsl{\'e}se},
	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 2011},
	year = {2011},
	month = {Jan 2011},
	pages = {471 - 480},
	publisher = {NJSZT},
	organization = {NJSZT},
	type = {Conference paper},
	address = {Szeged},
	author = {Attila Tanacs and Joakim Lindblad and Nata{\v s}a Sladoje and Zoltan Kato},
	editor = {Zoltan Kato and K{\'a}lm{\'a}n Pal{\'a}gyi}
}
@inbook {1224,
	title = {Estimation of linear deformations of 3D objects},
	booktitle = {IEEE International Conference on Image Processing (ICIP)},
	year = {2010},
	note = {UT: 000287728000038ScopusID: 78651064516doi: 10.1109/ICIP.2010.5650932},
	month = {Sep 2010},
	pages = {153 - 156},
	publisher = {IEEE},
	organization = {IEEE},
	type = {Conference paper},
	address = {Hong Kong, Hong Kong},
	abstract = {<p>We propose a registration method to find affine transformations between 3D objects by constructing and solving an overdetermined system of polynomial equations. We utilize voxel coverage information for more precise object boundary description. An iterative solution enables us to easily adjust the method to recover e.g. rigid-body and similarity transformations. Synthetic tests show the advantage of the voxel coverage representation, and reveal the robustness properties of our method against different types of segmentation errors. The method is tested on a real medical CT volume. {\textcopyright} 2010 IEEE.</p>},
	author = {Attila Tanacs and Joakim Lindblad and Nata{\v s}a Sladoje and Zoltan Kato}
}
@inbook {1225,
	title = {Recovering affine deformations of fuzzy shapes},
	booktitle = {Image Analysis},
	series = {Lecture Notes in Computer Science},
	number = {5575},
	year = {2009},
	note = {UT: 000268661000075ScopusID: 70350676212doi: 10.1007/978-3-642-02230-2_75},
	month = {June 2009},
	pages = {735 - 744},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	type = {Conference paper},
	address = {Oslo, Norway},
	abstract = {<p>Fuzzy sets and fuzzy techniques are attracting increasing attention nowadays in the field of image processing and analysis. It has been shown that the information preserved by using fuzzy representation based on area coverage may be successfully utilized to improve precision and accuracy of several shape descriptors; geometric moments of a shape are among them. We propose to extend an existing binary shape matching method to take advantage of fuzzy object representation. The result of a synthetic test show that fuzzy representation yields smaller registration errors in average. A segmentation method is also presented to generate fuzzy segmentations of real images. The applicability of the proposed methods is demonstrated on real X-ray images of hip replacement implants. {\textcopyright} 2009 Springer Berlin Heidelberg.</p>},
	doi = {10.1007/978-3-642-02230-2_75},
	author = {Attila Tanacs and Csaba Domokos and Nata{\v s}a Sladoje and Joakim Lindblad and Zoltan Kato},
	editor = {Arnt-Borre Salberg and Jon Yngve Hardeberg and Robert Jenssen}
}
