<?xml version="1.0" encoding="UTF-8"?><xml><records><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%">Péter Balázs</style></author><author><style face="normal" font="default" size="100%">Zoltán Ozsvár</style></author><author><style face="normal" font="default" size="100%">Tamás Sámuel Tasi</style></author><author><style face="normal" font="default" size="100%">László G Nyúl</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Measure of Directional Convexity Inspired by Binary Tomography</style></title><secondary-title><style face="normal" font="default" size="100%">Fundamenta Informaticae</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Oct 2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">141</style></volume><pages><style face="normal" font="default" size="100%">151-167</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span&gt;Inspired by binary tomography, we present a measure of directional convexity of binary images combining various properties of the configuration of 0s and 1s in the binary image. The measure can be supported by proper theory, is easy to compute, and as shown in our experiments, behaves intuitively. &lt;/span&gt;&lt;span class=&quot;below-fold&quot; data-below-fold=&quot;FI1269&quot;&gt;The measure can be useful in numerous applications of digital image processing and pattern recognition, and especially in binary tomography. We show in detail an application of this latter one, by providing a novel reconstruction algorithm for almost hv-convex binary images. We also present experimental results and mention some of the possible generalizations of the measure. &lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2-3</style></issue><work-type><style face="normal" font="default" size="100%">Journal article</style></work-type><section><style face="normal" font="default" size="100%">151</style></section></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%">Tamás Sámuel Tasi</style></author><author><style face="normal" font="default" size="100%">László Gábor Nyúl</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%">Gabriella Sanniti di Baja</style></author><author><style face="normal" font="default" size="100%">Jose Ruiz-Shulcloper</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Directional Convexity Measure for Binary Tomography</style></title><secondary-title><style face="normal" font="default" size="100%">Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/chapter/10.1007%2F978-3-642-41827-3_2</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Berlin; Heidelberg</style></pub-location><pages><style face="normal" font="default" size="100%">9 - 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;There is an increasing demand for a new measure of convexity fordiscrete sets for various applications. For example, the well- known measures for h-, v-, and hv-convexity of discrete sets in binary tomography pose rigorous criteria to be satisfied. Currently, there is no commonly accepted, unified view on what type of discrete sets should be considered nearly hv-convex, or to what extent a given discrete set can be considered convex, in case it does not satisfy the strict conditions. We propose a novel directional convexity measure for discrete sets based on various properties of the configuration of 0s and 1s in the set. It can be supported by proper theory, is easy to compute, and according to our experiments, it behaves intuitively. We expect it to become a useful alternative to other convexity measures in situations where the classical definitions cannot be used.&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: 84893169866doi: 10.1007/978-3-642-41827-3_2</style></notes></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%">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%">Extracting geometrical features of discrete images from their projections</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 of 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%">52</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%">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%">Tamás Sámuel Tasi</style></author><author><style face="normal" font="default" size="100%">M Hegedűs</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%">M Petrou</style></author><author><style face="normal" font="default" size="100%">A D Sappa</style></author><author><style face="normal" font="default" size="100%">A G Triantafyllidis</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Perimeter estimation of some discrete sets from horizontal and vertical projections</style></title><secondary-title><style face="normal" font="default" size="100%">IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA)</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%">IASTED ACTA Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Crete, Greek</style></pub-location><pages><style face="normal" font="default" size="100%">174 - 181</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 design neural networks to estimate the perimeter of simple and more complex discrete sets from their horizontal and vertical projections. The information extracted this way can be useful to simplify the problem of reconstructing the discrete set from its projections, which task is in focus of discrete tomography. Beside presenting experimental results with neural networks, we also reveal some statistical properties of the perimeter of the studied discrete sets.&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: 84864772360doi: 10.2316/P.2012.778-017</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><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%">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%">Obtaining geometrical properties of binary images from two projections using neural networks</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, Hungary</style></pub-location><pages><style face="normal" font="default" size="100%">69</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>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></records></xml>