<?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><author><style face="normal" font="default" size="100%">Gábor Németh</style></author><author><style face="normal" font="default" size="100%">Péter Kardos</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Reneta P Barneva</style></author><author><style face="normal" font="default" size="100%">Bhattacharya, B. B.</style></author><author><style face="normal" font="default" size="100%">Valentin E Brimkov</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Equivalent Sequential and Parallel Subiteration-Based Surface-Thinning Algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of Combinatorial Image Analysis: 17th International Workshop, IWCIA 2015</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%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Nov 2015</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%">Calcutta, India</style></pub-location><volume><style face="normal" font="default" size="100%">9448</style></volume><pages><style face="normal" font="default" size="100%">31-45</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-26144-7</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></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%">Antal Nagy</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Reneta P Barneva</style></author><author><style face="normal" font="default" size="100%">Valentin E Brimkov</style></author><author><style face="normal" font="default" size="100%">Josef Šlapal</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Smoothing Filters in the DART Algorithm</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Image Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May 2014</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/chapter/10.1007%2F978-3-319-07148-0_20</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%">May 2014, Brno, Czech Republic</style></pub-location><pages><style face="normal" font="default" size="100%">224 - 237</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>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><author><style face="normal" font="default" size="100%">Kálmán Palágyi</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Reneta P Barneva</style></author><author><style face="normal" font="default" size="100%">Valentin E Brimkov</style></author><author><style face="normal" font="default" size="100%">Jake K Aggarwal</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Binary image reconstruction from two projections and skeletal information</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial 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%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Nov 2012</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">7655</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%">263 - 273</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, the goal is to reconstruct binary images from a small set of their projections. However, especially when only two projections are used, the task can be extremely underdetermined. In this paper, we show how to reduce ambiguity by using the morphological skeleton of the image as a priori. Three different variants of our method based on Simulated Annealing are tested using artificial binary images, and compared by reconstruction time and error. © 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: 84869986820doi: 10.1007/978-3-642-34732-0_20</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%">László Gábor Varga</style></author><author><style face="normal" font="default" size="100%">Péter Balázs</style></author><author><style face="normal" font="default" size="100%">Antal Nagy</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Reneta P Barneva</style></author><author><style face="normal" font="default" size="100%">Valentin E Brimkov</style></author><author><style face="normal" font="default" size="100%">Herbert A Hauptman</style></author><author><style face="normal" font="default" size="100%">Renato M Natal Jorge</style></author><author><style face="normal" font="default" size="100%">João Manuel R S Tavares</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Direction-dependency of a binary tomographic reconstruction algorithm</style></title><secondary-title><style face="normal" font="default" size="100%">Computational Modeling of Objects Represented in Images</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%">May 2010</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">6026</style></number><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Buffalo, NY, USA</style></pub-location><pages><style face="normal" font="default" size="100%">242 - 253</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-12711-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;abstract-content formatted&quot; itemprop=&quot;description&quot;&gt;&lt;p class=&quot;a-plus-plus&quot;&gt;We study how the quality of an image reconstructed by a binary tomographic algorithm depends on the direction of the observed object in the scanner, if only a few projections are available. To do so we conduct experiments on a set of software phantoms by reconstructing them form different projection sets using an algorithm based on D.C. programming (a method for minimizing the difference of convex functions), and compare the accuracy of the corresponding reconstructions by two suitable approaches. Based on the experiments, we discuss consequences on applications arising from the field of non-destructive testing, as well.&lt;/p&gt;&lt;/div&gt;&lt;p&gt;&amp;nbsp;&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: 000279020400022ScopusID: 77952365308doi: 10.1007/978-3-642-12712-0_22</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></authors><secondary-authors><author><style face="normal" font="default" size="100%">Valentin E Brimkov</style></author><author><style face="normal" font="default" size="100%">Reneta P Barneva</style></author><author><style face="normal" font="default" size="100%">Herbert A Hauptman</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">On the number of hv-convex discrete sets</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial 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%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Apr 2008</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">4958</style></number><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Buffalo, NY, USA</style></pub-location><pages><style face="normal" font="default" size="100%">112 - 123</style></pages><isbn><style face="normal" font="default" size="100%">978-3-540-78274-2</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;One of the basic problems in discrete tomography is thereconstruction of discrete sets from few projections. Assuming that the set to be reconstructed fulfills some geometrical properties is a commonly used technique to reduce the number of possibly many different solutions of the same reconstruction problem. The class of hv-convex discrete sets and its subclasses have a well-developed theory. Several reconstruction algorithms as well as some complexity results are known for those classes. The key to achieve polynomial-time reconstruction of an hv- convex discrete set is to have the additional assumption that the set is connected as well. This paper collects several statistics on hv-convex discrete sets, which are of great importance in the analysis of algorithms for reconstructing such kind of discrete sets. © 2008 Springer-Verlag 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: 000254600100010ScopusID: 70249110264doi: 10.1007/978-3-540-78275-9_10</style></notes></record></records></xml>