<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Péter Balázs</style></author><author><style face="normal" font="default" size="100%">Mihály Gara</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Arnt-Borre Salberg</style></author><author><style face="normal" font="default" size="100%">Jon Yngve Hardeberg</style></author><author><style face="normal" font="default" size="100%">Robert Jenssen</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">An evolutionary approach for object-based image reconstruction using learnt priors</style></title><secondary-title><style face="normal" font="default" size="100%">Image Analysis</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title><short-title><style face="normal" font="default" size="100%">LNCS</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June 2009</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">5575</style></number><publisher><style face="normal" font="default" size="100%">Springer-Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Oslo, Norway</style></pub-location><pages><style face="normal" font="default" size="100%">520 - 529</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-02229-6</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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. © 2009 Springer Berlin Heidelberg.&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Conference paper</style></work-type><notes><style face="normal" font="default" size="100%">UT: 000268661000053ScopusID: 70350650400doi: 10.1007/978-3-642-02230-2_53</style></notes></record></records></xml>