<?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%">Milan Lesko</style></author><author><style face="normal" font="default" size="100%">Zoltan Kato</style></author><author><style face="normal" font="default" size="100%">Antal Nagy</style></author><author><style face="normal" font="default" size="100%">Imre Gombos</style></author><author><style face="normal" font="default" size="100%">Zsolt Török</style></author><author><style face="normal" font="default" size="100%">László Vígh</style></author><author><style face="normal" font="default" size="100%">László Vígh</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Aytul Ercil</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Live cell segmentation in fluorescence microscopy via graph cut</style></title><secondary-title><style face="normal" font="default" size="100%">20th international conference on pattern recognition (ICPR 2010)</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%">Aug 2010</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%">Istanbul, Turkey</style></pub-location><pages><style face="normal" font="default" size="100%">1485 - 1488</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4244-7542-1 </style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We propose a novel Markovian segmentation model which takes into account edge information. By construction, the model uses only pairwise interactions and its energy is submodular. Thus the exact energy minima is obtained via a max-flow/min-cut algorithm. The method has been quantitatively evaluated on synthetic images as well as on fluorescence microscopic images of live cells. © 2010 IEEE.&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%">11593484 </style></accession-num><notes><style face="normal" font="default" size="100%">ScopusID: 78149486419doi: 10.1109/ICPR.2010.367Besorolás: Konferenciaközlemény</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%">M Petrou</style></author><author><style face="normal" font="default" size="100%">T Saramaki</style></author><author><style face="normal" font="default" size="100%">Aytul Ercil</style></author><author><style face="normal" font="default" size="100%">Sven Lončarić</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Reconstructing some hv-convex binary images from three or four projections</style></title><secondary-title><style face="normal" font="default" size="100%">Proccedings of the 5th International Symposium on Image and Signal Processing and Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Sep 2007</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%">Istanbul, Turkey</style></pub-location><pages><style face="normal" font="default" size="100%">136 - 140</style></pages><isbn><style face="normal" font="default" size="100%">978-953-184-116-0 </style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The reconstruction of binary images from their projections is animportant problem in discrete tomography. The main challenge in this task is that in certain cases the projections do not uniquely determine the binary image. This can yield an extremely large number of (sometimes very different) solutions. Moreover, under certain circumstances the reconstruction becomes NP-hard. A commonly used technique to reduce ambiguity and to avoid intractability is to suppose that the image to be reconstructed arises from a certain class of images having some geometrical properties. This paper studies the reconstruction problem in the class of hv-convex images having their components in so-called decomposable configurations. First, we give a negative result showing that there can be exponentially many images of the above class having the same three projections. Then, we present a heuristic that uses four projections to reconstruct an hv-convex image with decomposable configuration. We also analyze the performance of our heuristic from the viewpoints of accuracy and running time.&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: 000253387900025ScopusID: 7949129892doi: 10.1109/ISPA.2007.4383678</style></notes></record></records></xml>