<?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%">Kálmán Palágyi</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Jose Ruiz-Shulcloper</style></author><author><style face="normal" font="default" size="100%">Gabriella Sanniti di Baja</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Deletion Rules for Equivalent Sequential and Parallel Reductions</style></title><secondary-title><style face="normal" font="default" size="100%">Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title><alt-title><style face="normal" font="default" size="100%">LNCS</style></alt-title><short-title><style face="normal" font="default" size="100%">Conference Paper</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Nov 2013</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/chapter/10.1007%2F978-3-642-41822-8_3</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Berlin; Heidelberg</style></pub-location><pages><style face="normal" font="default" size="100%">17 - 24</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-41821-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A reduction operator transforms a binary picture only by changing some black points to white ones, which is referred to as deletion. Sequential reductions may delete just one point at a time, while parallel reductions can alter a set of points simultaneously. Two reductions are called equivalent if they produce the same result for each input picture. This work lays a bridge between the parallel and the sequential strategies. A class of deletion rules are proposed that provide 2D parallel reductions being equivalent to sequential reductions. Some new sufficient conditions for topology-preserving parallel reductions are also reported.&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%">Lecture Notes in Computer Science, Vol. 8258</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%">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>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Norbert Hantos</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%">Jose Ruiz-Shulcloper</style></author><author><style face="normal" font="default" size="100%">Gabriella Sanniti di Baja</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Reconstruction and Enumeration of hv-Convex Polyominoes with Given Horizontal Projection</style></title><secondary-title><style face="normal" font="default" size="100%">Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (CIARP)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title><alt-title><style face="normal" font="default" size="100%">LNCS</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Nov 2013</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">8258</style></number><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg; London; New York</style></pub-location><pages><style face="normal" font="default" size="100%">100 - 107</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-41821-1</style></isbn><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><notes><style face="normal" font="default" size="100%">ScopusID: 84893181366doi: 10.1007/978-3-642-41822-8_13</style></notes></record></records></xml>