<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mihály Gara</style></author><author><style face="normal" font="default" size="100%">Péter Balázs</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Artificial intelligence methods in discrete tomography</style></title><secondary-title><style face="normal" font="default" size="100%">Conference of PhD students in computer science. Volume of Extended Abstracts.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June 2012</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">University Szeged, Institute of Informatics</style></publisher><pub-location><style face="normal" font="default" size="100%">Szeged</style></pub-location><pages><style face="normal" font="default" size="100%">16</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Tomography is an imaging procedure to examine the internal structure of objects. The crosssection&lt;br&gt;images are constructed with the aid of the object’s projections. It is often necessary to&lt;br&gt;minimize the number of those projections to avoid the damage or destruction of the examined&lt;br&gt;object, since in most cases the projections are made by destructive rays.&lt;br&gt;Sometimes the number of available projections are so small that conventional methods cannot&lt;br&gt;provide satisfactory results. In these cases Discrete Tomograpy can provide acceptable solutions,&lt;br&gt;but it can only be used with the assumption the object is made of only a few materials,&lt;br&gt;thus only a small number of intensity values appear in the reconstructed cross-section image.&lt;br&gt;Although there are a lot of discrete tomographic reconstruction algorithms, only a few papers&lt;br&gt;deal with the determination of intensity values of the image, in advance. In our work we&lt;br&gt;try to fill this gap by using different learning methods. During the learning and classification&lt;br&gt;we used the projection values as input arguments.&lt;br&gt;In the second part of our talk we concentrate on Binary Tomography (a special kind of Discrete&lt;br&gt;Tomography)where it is supposed that the object is composed of onematerial. Thus, there&lt;br&gt;can be only two intensities on the cross-section image - one for the object points and one for&lt;br&gt;the background. Here, we compared our earlier presented binary tomographic evolutionary&lt;br&gt;reconstruction algorithm to two others. We present the details of the above-mentioned reconstruction&lt;br&gt;method and our experimental results. This paper is based on our previous works.&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Abstract</style></work-type></record><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%">Mihály Gara</style></author><author><style face="normal" font="default" size="100%">Tamás Sámuel Tasi</style></author><author><style face="normal" font="default" size="100%">Péter Balázs</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Annick Montanvert</style></author><author><style face="normal" font="default" size="100%">Pierre Soille</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Machine learning as a preprocessing phase in discrete tomography</style></title><secondary-title><style face="normal" font="default" size="100%">Applications of Discrete Geometry and Mathematical Morphology (WADGMM)</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%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Aug 2012</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">7346</style></number><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Berlin; Heidelberg; New York; London; Paris; Tokyo</style></pub-location><pages><style face="normal" font="default" size="100%">109 - 124</style></pages><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 investigate for two well-known machine learning methods, decision trees and neural networks, how they classify discrete images from their projections. As an example, we present classification results when the task is to guess the number of intensity values of the discrete image. Machine learning can be used in Discrete Tomography as a preprocessing step in order to choose the proper reconstruction algorithm or - with the aid of the knowledge acquired - to improve its accuracy. We also show how to design new evolutionary reconstruction methods that can exploit the information gained by machine learning classifiers. © 2012 Springer-Verlag.&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%">ScopusID: 84865454250doi: 10.1007/978-3-642-32313-3_8</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mihály Gara</style></author><author><style face="normal" font="default" size="100%">Péter Balázs</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Zoltan Kato</style></author><author><style face="normal" font="default" size="100%">Kálmán Palágyi</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Bináris tomográfiai rekonstrukció objektum alapú evolúciós algoritmussal</style></title><secondary-title><style face="normal" font="default" size="100%">A Képfeldolgozók és Alakfelismerők Társaságának konferenciája - KÉPAF 2011</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan 2011</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">NJSZT</style></publisher><pub-location><style face="normal" font="default" size="100%">Szeged</style></pub-location><pages><style face="normal" font="default" size="100%">117 - 127</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Conference paper</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mihály Gara</style></author><author><style face="normal" font="default" size="100%">Péter Balázs</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Binary tomographic reconstruction with an object-based evolutionary algorithm</style></title><secondary-title><style face="normal" font="default" size="100%">Conference of PhD Students in Computer Science. Volume of Extended Abstracts</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June 2010</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">University of Szeged</style></publisher><pub-location><style face="normal" font="default" size="100%">Szeged</style></pub-location><pages><style face="normal" font="default" size="100%">31</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Abstract</style></work-type></record><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><author><style face="normal" font="default" size="100%">Tamás Sámuel Tasi</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Annick Montanvert</style></author><author><style face="normal" font="default" size="100%">Pierre Soille</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Machine learning for supporting binary tomographic reconstruction</style></title><secondary-title><style face="normal" font="default" size="100%">Workshop on Applications of Discrete Geometry in Mathematical Morphology</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%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Aug 2010</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Istambul, Turkey</style></pub-location><pages><style face="normal" font="default" size="100%">101 - 105</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Conference paper</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mihály Gara</style></author><author><style face="normal" font="default" size="100%">Péter Balázs</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Dmitrij Chetverikov</style></author><author><style face="normal" font="default" size="100%">Tamas Sziranyi</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Döntési fákon alapuló előfeldolgozás a bináris tomográfiában</style></title><secondary-title><style face="normal" font="default" size="100%">A Képfeldolgozók és Alakfelismerők Társaságának konferenciája - KÉPAF 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan 2009</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Akaprint</style></publisher><pub-location><style face="normal" font="default" size="100%">Budapest</style></pub-location><pages><style face="normal" font="default" size="100%">nincs számozás</style></pages><language><style face="normal" font="default" size="100%">hun</style></language><work-type><style face="normal" font="default" size="100%">Conference paper</style></work-type><notes><style face="normal" font="default" size="100%">8 pages</style></notes></record><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><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mihály Gara</style></author><author><style face="normal" font="default" size="100%">Tamás Sámuel Tasi</style></author><author><style face="normal" font="default" size="100%">Péter Balázs</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning connectedness and convexity of binary images from their projections</style></title><secondary-title><style face="normal" font="default" size="100%">PURE MATHEMATICS AND APPLICATIONS</style></secondary-title><short-title><style face="normal" font="default" size="100%">PU.M.A PURE MATH APPL</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%">2009</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">27 - 48</style></pages><isbn><style face="normal" font="default" size="100%">1218-4586</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1-2</style></issue><work-type><style face="normal" font="default" size="100%">Journal article</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mihály Gara</style></author><author><style face="normal" font="default" size="100%">Zoltan Kato</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Supervised Color Image Segmentation in a Markovian Framework</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009///</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.inf.u-szeged.hu/~kato/software/colormrfdemo.html</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This is the sample implementation of a Markov random field based color image segmentation algorithm described in the following paper: Zoltan Kato, Ting Chuen Pong, and John Chung Mong Lee. Color Image Segmentation and Parameter Estimation in a Markovian Framework. Pattern Recognition Letters, 22(3-4):309--321, March 2001. Note that the current demo program implements only a supervised version of the segmentation method described in the above paper (i.e. parameter values are learned interactively from representative regions selected by the user). Otherwise, the program implements exactly the color MRF model proposed in the paper. Images are automatically converted from RGB to the perceptually uniform CIE-L*u*v* color space before segmentation.&lt;/p&gt;</style></abstract></record><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></contributors><titles><title><style face="normal" font="default" size="100%">Decision trees in binary tomography for supporting the reconstruction of hv-convex connected images</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Advanced Concepts for Intelligent Vision Systems</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%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Oct 2008</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">5259</style></number><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Juan-les-Pins, France</style></pub-location><volume><style face="normal" font="default" size="100%">5259</style></volume><pages><style face="normal" font="default" size="100%">433-443</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In binary tomography, several algorithms are known for reconstructing binary images having some geometrical properties from their projections. In order to choose the appropriate reconstruction algorithm it is necessary to have a priori information of the image to be reconstructed. In this way we can improve the speed and reduce the ambiguity of the reconstruction. Our work is concerned with the problem of retrieving geometrical information from the projections themselves. We investigate whether it is possible to determine geometric features of binary images if only their projections are known. Most of the reconstruction algorithms based on geometrical information suppose $hv$-convexity or connectedness about the image to be reconstructed. We investigate those properties in detail, and also the task of separating 4- and 8-connected images. We suggest decision trees for the classification, and show some preliminary experimental results of applying them for the class of $hv$-convex and connected discrete sets. &lt;tt&gt; &lt;/tt&gt;&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Conference Paper</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mihály Gara</style></author><author><style face="normal" font="default" size="100%">Péter Balázs</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Kálmán Palágyi</style></author><author><style face="normal" font="default" size="100%">Balázs Bánhelyi</style></author><author><style face="normal" font="default" size="100%">Tamás Gergely</style></author><author><style face="normal" font="default" size="100%">István Matievics</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Determination of geometric features of binary images from their projections by using decision trees</style></title><secondary-title><style face="normal" font="default" size="100%">Conference of PhD Students in Computer Science. Volume of Extended Abstracts</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">July 2008</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">University of Szeged</style></publisher><pub-location><style face="normal" font="default" size="100%">Szeged, Hungary</style></pub-location><pages><style face="normal" font="default" size="100%">26</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Abstract</style></work-type></record></records></xml>