@inbook {1135,
	title = {An evolutionary approach for object-based image reconstruction using learnt priors},
	booktitle = {Image Analysis},
	series = {Lecture Notes in Computer Science},
	number = {5575},
	year = {2009},
	note = {UT: 000268661000053ScopusID: 70350650400doi: 10.1007/978-3-642-02230-2_53},
	month = {June 2009},
	pages = {520 - 529},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	type = {Conference paper},
	address = {Oslo, Norway},
	abstract = {<p>In this paper we present a novel algorithm for reconstructingbinary images containing objects which can be described by some parameters. In particular, we investigate the problem of reconstructing binary images representing disks from four projections. We develop a genetic algorithm for this and similar problems. We also discuss how prior information on the number of disks can be incorporated into the reconstruction in order to obtain more accurate images. In addition, we present a method to exploit such kind of knowledge from the projections themselves. Experiments on artificial data are also conducted. {\textcopyright} 2009 Springer Berlin Heidelberg.</p>},
	isbn = {978-3-642-02229-6},
	doi = {10.1007/978-3-642-02230-2_53},
	author = {P{\'e}ter Bal{\'a}zs and Mih{\'a}ly Gara},
	editor = {Arnt-Borre Salberg and Jon Yngve Hardeberg and Robert Jenssen}
}
@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}
}
