<?xml version="1.0" encoding="UTF-8"?><xml><records><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%">Gábor Németh</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%">László Czúni</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Parallel Thinning Based on Isthmuses</style></title><secondary-title><style face="normal" font="default" size="100%">A Képfeldolgozók és Alakfelismerők Társaságának konferenciája - KÉPAF 2013</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%">Jan 2013</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">NJSZT-KÉPAF</style></publisher><pub-location><style face="normal" font="default" size="100%">Veszprém</style></pub-location><pages><style face="normal" font="default" size="100%">512 - 525</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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tibor Dobján</style></author><author><style face="normal" font="default" size="100%">Gábor Németh</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spectrum Skeletonization: A New Method for Acoustic Signal Feature Extraction.</style></title><secondary-title><style face="normal" font="default" size="100%">ACTA CYBERNETICA-SZEGED</style></secondary-title><short-title><style face="normal" font="default" size="100%">ACTA CYBERN-SZEGED</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%">2013</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><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">89 - 103</style></pages><isbn><style face="normal" font="default" size="100%">0324-721X</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Vibration Analysis Tests (VAT) and Acoustic Emission tests (AE) are used in several industrial applications. Many of them perform analysis in the frequency domain. Peaks in the power density spectrum hold relevant information about acoustic events. In this paper we propose a novel method for feature extraction of vibration samples by analyzing the shape of their auto power spectrum density function. The approach uses skeletonization techniques in order to find the hierarchical structure of the spectral peaks. The proposed method can be applied as a preprocessing step for spectrum analysis of vibration signals. &lt;tt&gt; &lt;/tt&gt;&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Journal article</style></work-type><notes><style face="normal" font="default" size="100%">#Szerző vagy Forráskiadás készítője vagy Kritikai kiadás készítője ismeretlen</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%">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%">Valentin E Brimkov</style></author><author><style face="normal" font="default" size="100%">Reneta P Barneva</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Topology Preserving Parallel 3D Thinning Algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">Digital Geometry Algorithms</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computational Vision and Biomechanics</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">2</style></number><publisher><style face="normal" font="default" size="100%">Springer-Verlag</style></publisher><pages><style face="normal" font="default" size="100%">165 - 188</style></pages><isbn><style face="normal" font="default" size="100%">978-94-007-4173-7</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 widely used technique to obtain skeletons of binary objects is thinning, which is an iterative layer-by-layer erosion in a topology preserving way. Thinning in 3D is capable of extracting various skeleton-like shape descriptors (i.e., centerlines, medial surfaces, and topological kernels). This chapter describes a family of new parallel 3D thinning algorithms for (26, 6) binary pictures. The reported algorithms are derived from some sufficient conditions for topology preserving parallel reduction operations, hence their topological correctness is guaranteed. &lt;tt&gt; &lt;/tt&gt;&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Book chapter</style></work-type><notes><style face="normal" font="default" size="100%">doi: 10.1007/978-94-007-4174-4_6</style></notes><section><style face="normal" font="default" size="100%">6</style></section></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%">Gábor Németh</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%">Sven Lončarić</style></author><author><style face="normal" font="default" size="100%">Giovanni Ramponi</style></author><author><style face="normal" font="default" size="100%">D. Sersic</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">2D Parallel Thinning Algorithms Based on Isthmus-Preservation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the International Symposium on Image and Signal Processing and Analysis (ISPA)</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%">Sep 2011 </style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Dubrovnik, Croatia</style></pub-location><pages><style face="normal" font="default" size="100%">585 - 590</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4577-0841-1 </style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Skeletons are widely used shape descriptors which summarize the general form of binary objects. A technique to obtain skeletons is the thinning, that is an iterative layer-by-layer erosion in a topology-preserving way. Conventional thinning algorithms preserve line endpoints to provide important geometric information relative to the object to be represented. Bertrand and Couprie proposed an alternative strategy by accumulating isthmus points that are line interior points. In this paper we present six new 2D parallel thinning algorithms that are derived from some sufficient conditions for topology preserving reductions and based on isthmus-preservation.&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Conference paper</style></work-type><accession-num><style face="normal" font="default" size="100%">12307467 </style></accession-num><notes><style face="normal" font="default" size="100%">ScopusID: 83455172782</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%">Gábor Németh</style></author><author><style face="normal" font="default" size="100%">Péter Kardos</style></author><author><style face="normal" font="default" size="100%">Kálmán Palágyi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">2D parallel thinning and shrinking based on sufficient conditions for topology preservation</style></title><secondary-title><style face="normal" font="default" size="100%">ACTA CYBERNETICA-SZEGED</style></secondary-title><short-title><style face="normal" font="default" size="100%">ACTA CYBERN-SZEGED</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011</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><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">125 - 144</style></pages><isbn><style face="normal" font="default" size="100%">0324-721X</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Thinning and shrinking algorithms, respectively, are capable of extracting medial lines and topological kernels from digital binary objects in a topology preserving way. These topological algorithms are composed of reduction operations: object points that satisfy some topological and geometrical constraints are removed until stability is reached. In this work we present some new sufficient conditions for topology preserving parallel reductions and fiftyfour new 2D parallel thinning and shrinking algorithms that are based on our conditions. The proposed thinning algorithms use five characterizations of endpoints.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><work-type><style face="normal" font="default" size="100%">Journal article</style></work-type><notes><style face="normal" font="default" size="100%">ScopusID: 79960666919</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%">Gábor Németh</style></author><author><style face="normal" font="default" size="100%">Péter Kardos</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%">Jake K Aggarwal</style></author><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%">Kostadin N Koroutchev</style></author><author><style face="normal" font="default" size="100%">Elka R Korutcheva</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A family of topology-preserving 3d parallel 6-subiteration thinning algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Image Analysis (IWCIA)</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%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May 2011</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">6636</style></number><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Madrid, Spain</style></pub-location><pages><style face="normal" font="default" size="100%">17 - 30</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-21072-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Thinning is an iterative layer-by-layer erosion until only the skeleton-like shape features of the objects are left. This paper presents a family of new 3D parallel thinning algorithms that are based on our new sufficient conditions for 3D parallel reduction operators to preserve topology. The strategy which is used is called subiteration-based: each iteration step is composed of six parallel reduction operators according to the six main directions in 3D. The major contributions of this paper are: 1) Some new sufficient conditions for topology preserving parallel reductions are introduced. 2) A new 6-subiteration thinning scheme is proposed. Its topological correctness is guaranteed, since its deletion rules are derived from our sufficient conditions for topology preservation. 3) The proposed thinning scheme with different characterizations of endpoints yields various new algorithms for extracting centerlines and medial surfaces from 3D binary pictures. © 2011 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%">ScopusID: 79957651399doi: 10.1007/978-3-642-21073-0_5</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%">Péter Kardos</style></author><author><style face="normal" font="default" size="100%">Gábor Németh</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%">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%">Iterációnkénti simítással kombinált vékonyítás</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><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.inf.u-szeged.hu/kepaf2011/pdfs/S05_01.pdf</style></url></web-urls></urls><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%">174 - 189</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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gábor Németh</style></author><author><style face="normal" font="default" size="100%">Péter Kardos</style></author><author><style face="normal" font="default" size="100%">Kálmán Palágyi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Thinning combined with iteration-by-iteration smoothing for 3D binary images</style></title><secondary-title><style face="normal" font="default" size="100%">GRAPHICAL MODELS</style></secondary-title><short-title><style face="normal" font="default" size="100%">GRAPH MODELS</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Nov 2011</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">73</style></volume><pages><style face="normal" font="default" size="100%">335 - 345</style></pages><isbn><style face="normal" font="default" size="100%">1524-0703</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 work we present a new thinning scheme for reducing the noise sensitivity of 3D thinning algorithms. It uses iteration-by-iteration smoothing that removes some border points that are considered as extremities. The proposed smoothing algorithm is composed of two parallel topology preserving reduction operators. An efficient implementation of our algorithm is sketched and its topological correctness for (26, 6) pictures is proved. © 2011 Elsevier Inc. All rights reserved.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><work-type><style face="normal" font="default" size="100%">Journal article</style></work-type><notes><style face="normal" font="default" size="100%">ScopusID: 79952613010doi: 10.1016/j.gmod.2011.02.001</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%">Gábor Németh</style></author><author><style face="normal" font="default" size="100%">Péter Kardos</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%">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%">A topológia-megőrzés elegendő feltételein alapuló 3D párhuzamos vékonyító algoritmusok</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><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.inf.u-szeged.hu/kepaf2011/pdfs/S05_02.pdf</style></url></web-urls></urls><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%">190 - 205</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></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%">Gábor Németh</style></author><author><style face="normal" font="default" size="100%">Kálmán Palágyi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Topology Preserving Parallel Thinning Algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY</style></secondary-title><short-title><style face="normal" font="default" size="100%">INT J IMAG SYST TECH</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Feb 2011</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Wiley Periodicals, Inc.</style></publisher><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">37 - 44</style></pages><isbn><style face="normal" font="default" size="100%">0899-9457</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Thinning is an iterative object reduction technique for extracting medial curves from binary objects. During a thinning process, some border points that satisfy certain topological and geometric constraints are deleted in iteration steps. Parallel thinning algorithms are composed of parallel reduction operators that delete a set of object points simultaneously. This article presents 21 parallel thinning algorithms for (8,4) binary pictures that are derived from the sufficient conditions for topology preservation accommodated to the three parallel thinning approaches. © 2011 Wiley Periodicals, Inc.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><work-type><style face="normal" font="default" size="100%">Journal article</style></work-type><notes><style face="normal" font="default" size="100%">UT: 000287789100005ScopusID: 79951782238doi: 10.1002/ima.20272</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%">Péter Kardos</style></author><author><style face="normal" font="default" size="100%">Gábor Németh</style></author><author><style face="normal" font="default" size="100%">Kálmán Palágyi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bejárásfüggetlen szekvenciális vékonyítás</style></title><secondary-title><style face="normal" font="default" size="100%">ALKALMAZOTT MATEMATIKAI LAPOK</style></secondary-title><short-title><style face="normal" font="default" size="100%">ALKALMAZOTT MATEMATIKAI LAPOK</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%">2010</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">17 - 40</style></pages><isbn><style face="normal" font="default" size="100%">0133-3399</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gábor Németh</style></author><author><style face="normal" font="default" size="100%">Péter Kardos</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%">B Zagar</style></author><author><style face="normal" font="default" size="100%">A Kuijper</style></author><author><style face="normal" font="default" size="100%">H Sahbi</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Topology preserving 2-subfield 3D thinning algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA)</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%">Feb 2010</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%">Innsbruck, Austria</style></pub-location><pages><style face="normal" font="default" size="100%">310 - 316</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper presents a new family of 3D thinning algorithms for extracting skeleton-like shape features (i.e, centerline, medial surface, and topological kernel) from volumetric images. A 2-subfield strategy is applied: all points in a 3D picture are partitioned into two subsets which are alternatively activated. At each iteration, a parallel operator is applied for deleting some border points in the active subfield. The proposed algorithms are derived from Ma's sufficient conditions for topology preservation, and they use various endpoint characterizations.&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: 77954590365</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%">Gábor Németh</style></author><author><style face="normal" font="default" size="100%">Péter Kardos</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%">Aurélio Campilho</style></author><author><style face="normal" font="default" size="100%">Mohamed Kamel</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Topology Preserving 3D Thinning Algorithms using Four and Eight Subfields</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the International Conference on Image Analysis and Recognition (ICIAR)</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%">June 2010</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Póvoa de Varzim, Portugal</style></pub-location><volume><style face="normal" font="default" size="100%">6111</style></volume><pages><style face="normal" font="default" size="100%">316 - 325</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Thinning is a frequently applied technique for extracting skeleton-like shape features (i.e., centerline, medial surface, and topological kernel) from volumetric binary images. Subfield-based thinning algorithms partition the image into some subsets which are alternatively activated, and some points in the active subfield are deleted. This paper presents a set of new 3D parallel subfield-based thinning algorithms that use four and eight subfields. The three major contributions of this paper are: 1) The deletion rules of the presented algorithms are derived from some sufficient conditions for topology preservation. 2) A novel thinning scheme is proposed that uses iteration-level endpoint checking. 3) Various characterizations of endpoints yield different algorithms. © 2010 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: 77955432947doi: 10.1007/978-3-642-13772-3_32</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%">Gábor Németh</style></author><author><style face="normal" font="default" size="100%">Péter Kardos</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%">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%">Topology Preserving Parallel Smoothing for 3D Binary Images</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Computational Modeling of Objects Represented in Images (CMORI)</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%">May 2010</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Buffalo, USA</style></pub-location><volume><style face="normal" font="default" size="100%">6026</style></volume><pages><style face="normal" font="default" size="100%">287 - 298</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper presents a new algorithm for smoothing 3D binary images in a topology preserving way. Our algorithm is a reduction operator: some border points that are considered as extremities are removed. The proposed method is composed of two parallel reduction operators. We are to apply our smoothing algorithm as an iteration-by-iteration pruning for reducing the noise sensitivity of 3D parallel surface-thinning algorithms. An efficient implementation of our algorithm is sketched and its topological correctness for (26,6) pictures is proved. © 2010 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: 77952401887doi: 10.1007/978-3-642-12712-0_26</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%">Tobias Heimann</style></author><author><style face="normal" font="default" size="100%">Brahm Van Ginneken</style></author><author><style face="normal" font="default" size="100%">Martin A Styner</style></author><author><style face="normal" font="default" size="100%">Yulia Arzhaeva</style></author><author><style face="normal" font="default" size="100%">Volker Aurich</style></author><author><style face="normal" font="default" size="100%">Christian Bauer</style></author><author><style face="normal" font="default" size="100%">Andreas Beck</style></author><author><style face="normal" font="default" size="100%">Christoph Becker</style></author><author><style face="normal" font="default" size="100%">Reinhardt Beichel</style></author><author><style face="normal" font="default" size="100%">György Bekes</style></author><author><style face="normal" font="default" size="100%">Fernando Bello</style></author><author><style face="normal" font="default" size="100%">Gerd Binnig</style></author><author><style face="normal" font="default" size="100%">Horst Bischof</style></author><author><style face="normal" font="default" size="100%">Alexander Bornik</style></author><author><style face="normal" font="default" size="100%">Peter MM Cashman</style></author><author><style face="normal" font="default" size="100%">Ying Chi</style></author><author><style face="normal" font="default" size="100%">Andres Córdova</style></author><author><style face="normal" font="default" size="100%">Benoit M Dawant</style></author><author><style face="normal" font="default" size="100%">Márta Fidrich</style></author><author><style face="normal" font="default" size="100%">Jacob D Furst</style></author><author><style face="normal" font="default" size="100%">Daisuke Furukawa</style></author><author><style face="normal" font="default" size="100%">Lars Grenacher</style></author><author><style face="normal" font="default" size="100%">Joachim Hornegger</style></author><author><style face="normal" font="default" size="100%">Dagmar Kainmüller</style></author><author><style face="normal" font="default" size="100%">Richard I Kitney</style></author><author><style face="normal" font="default" size="100%">Hidefumi Kobatake</style></author><author><style face="normal" font="default" size="100%">Hans Lamecker</style></author><author><style face="normal" font="default" size="100%">Thomas Lange</style></author><author><style face="normal" font="default" size="100%">Jeongjin Lee</style></author><author><style face="normal" font="default" size="100%">Brian Lennon</style></author><author><style face="normal" font="default" size="100%">Rui Li</style></author><author><style face="normal" font="default" size="100%">Senhu Li</style></author><author><style face="normal" font="default" size="100%">Hans-Peter Meinzer</style></author><author><style face="normal" font="default" size="100%">Gábor Németh</style></author><author><style face="normal" font="default" size="100%">Daniela S Raicu</style></author><author><style face="normal" font="default" size="100%">Anne-Mareike Rau</style></author><author><style face="normal" font="default" size="100%">Eva M Van Rikxoort</style></author><author><style face="normal" font="default" size="100%">Mikael Rousson</style></author><author><style face="normal" font="default" size="100%">László Ruskó</style></author><author><style face="normal" font="default" size="100%">Kinda A Saddi</style></author><author><style face="normal" font="default" size="100%">Günter Schmidt</style></author><author><style face="normal" font="default" size="100%">Dieter Seghers</style></author><author><style face="normal" font="default" size="100%">Akinobi Shimizu</style></author><author><style face="normal" font="default" size="100%">Pieter Slagmolen</style></author><author><style face="normal" font="default" size="100%">Erich Sorantin</style></author><author><style face="normal" font="default" size="100%">Grzegorz Soza</style></author><author><style face="normal" font="default" size="100%">Ruchaneewan Susomboon</style></author><author><style face="normal" font="default" size="100%">Jonathan M Waite</style></author><author><style face="normal" font="default" size="100%">Andreas Wimmer</style></author><author><style face="normal" font="default" size="100%">Ivo Wolf</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison and evaluation of methods for liver segmentation from CT datasets</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE TRANSACTIONS ON MEDICAL IMAGING</style></secondary-title><short-title><style face="normal" font="default" size="100%">IEEE T MED IMAGING</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%">Aug 2009</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Price, K., Anything you can do, I can do better (no you can't) (1986) Comput. Vis. Graph. Image Process, 36 (2-3), pp. 387-391;S. G. Armato, G. McLennan, M. F. McNitt-Gray, C. R. Meyer, D. Yankelevitz, D. R. Aberle, C. I. Henschke, E. A. Hoffman, E. A. Ka</style></pub-location><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">1251 - 1265</style></pages><isbn><style face="normal" font="default" size="100%">0278-0062</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the &quot;MICCAI 2007 Grand Challenge&quot; workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques. © 2009 IEEE.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">8</style></issue><work-type><style face="normal" font="default" size="100%">Journal article</style></work-type><notes><style face="normal" font="default" size="100%">ScopusID: 68249121543doi: 10.1109/TMI.2009.2013851</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%">Kálmán Palágyi</style></author><author><style face="normal" font="default" size="100%">Gábor Németh</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Srecko Brlek</style></author><author><style face="normal" font="default" size="100%">Christophe Reutenauer</style></author><author><style face="normal" font="default" size="100%">Xavier Provençal</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Fully Parallel 3D Thinning Algorithms based on Sufficient Conditions for Topology Preservation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of Discrete Geometry for Computer Imagery (DGCI)</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%">Sep 2009</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">5810</style></number><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Montreal, Quebec, Canada</style></pub-location><pages><style face="normal" font="default" size="100%">481 - 492</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-04396-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper presents a family of parallel thinning algorithms for extracting medial surfaces from 3D binary pictures. The proposed algorithms are based on sufficient conditions for 3D parallel reduction operators to preserve topology for (26,6) pictures. Hence it is self-evident that our algorithms are topology preserving. Their efficient implementation on conventional sequential computers is also presented. © 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%">ScopusID: 77952414581doi: 10.1007/978-3-642-04397-0_41</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%">Péter Kardos</style></author><author><style face="normal" font="default" size="100%">Gábor Németh</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%">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%">Kritikus párokat vizsgáló bejárásfüggetlen szekvenciális vékonyító algoritmus</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%">1 - 8</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></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%">Gábor Németh</style></author><author><style face="normal" font="default" size="100%">György Kovács</style></author><author><style face="normal" font="default" size="100%">Kálmán Palágyi</style></author><author><style face="normal" font="default" size="100%">Attila Fazekas</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%">A morfológiai váz általánosítása szomszédsági szekvenciákkal</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%">1 - 10</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%">Péter Kardos</style></author><author><style face="normal" font="default" size="100%">Gábor Németh</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%">Petra Wiederhold</style></author><author><style face="normal" font="default" size="100%">Reneta P Barneva</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">An order-independent sequential thinning algorithm</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the International Workshop on Combinatorial Image Analysis (IWCIA)</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%">Nov 2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/chapter/10.1007/978-3-642-10210-3_13</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">5852</style></number><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Playa del Carmen, Mexico</style></pub-location><pages><style face="normal" font="default" size="100%">162 - 175</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-10208-0</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Thinning is a widely used approach for skeletonization. Sequential thinning algorithms use contour tracking: they scan border points and remove the actual one if it is not designated a skeletal point. They may produce various skeletons for different visiting orders. In this paper, we present a new 2-dimensional sequential thinning algorithm, which produces the same result for arbitrary visiting orders and it is capable of extracting maximally thinned skeletons. © Springer-Verlag Berlin Heidelberg 2009.&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: 78650496028doi: 10.1007/978-3-642-10210-3_13</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%">Attila Fazekas</style></author><author><style face="normal" font="default" size="100%">Kálmán Palágyi</style></author><author><style face="normal" font="default" size="100%">György Kovács</style></author><author><style face="normal" font="default" size="100%">Gábor Németh</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Antonios Gasteratos</style></author><author><style face="normal" font="default" size="100%">Markus Vincze</style></author><author><style face="normal" font="default" size="100%">John K Tsotsos</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Skeletonization based on metrical neighborhood sequences</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision Systems</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%">May 2008</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">5008</style></number><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Santorini, Greece</style></pub-location><pages><style face="normal" font="default" size="100%">333 - 342</style></pages><isbn><style face="normal" font="default" size="100%">978-3-540-79546-9</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Skeleton is a shape descriptor which summarizes the general formof objects. It can be expressed in terms of the fundamental morphological operations. The limitation of that characterization is that its construction based on digital disks such that cannot provide good approximation to the Euclidean disks. In this paper we define a new type of skeleton based on neighborhood sequences that is much closer to the Euclidean skeleton. A novel method for quantitative comparison of skeletonization algorithms is also proposed. © 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%">ScopusID: 44649159529doi: 10.1007/978-3-540-79547-6</style></notes></record></records></xml>