<?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></records></xml>