<?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%">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%">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%">Isthmus-based Order-Independent Sequential Thinning</style></title><secondary-title><style face="normal" font="default" size="100%">IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SSPRA)</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><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.actapress.com/Content_of_Proceeding.aspx?proceedingID=736</style></url></web-urls></urls><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%">28 - 34</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 as a layer-by-layer reduction is a frequently used technique for skeletonization. Sequential thinning algorithms usually suffer from the drawback of being order-dependent, i.e., their results depend on the visiting order of object points. Earlier order-independent sequential methods are based on the conventional thinning schemes that preserve endpoints to provide relevant geometric information of objects. These algorithms can generate centerlines in 2D and medial surfaces in 3D. This paper presents an alternative strategy for order-independent thinning which follows an approach, proposed by Bertrand and Couprie, which accumulates so-called isthmus points. The main advantage of this order-independent strategy over the earlier ones is that it makes also possible to produce centerlines of 3D objects.&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%">doi: 10.2316/P.2012.778-025</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></records></xml>