<?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%">Vicente, Sara</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Graph cut based image segmentation with connectivity priors</style></title><secondary-title><style face="normal" font="default" size="100%">26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><isbn><style face="normal" font="default" size="100%">9781424422432</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Graph cut is a popular technique for interactive image segmentation. However, it has certain shortcomings. In particular, graph cut has problems with segmenting thin elongated objects due to the &quot;shrinking bias&quot;. To overcome this problem, we propose to impose an additional connectivity prior, which is a very natural assumption about objects. We formulate several versions of the connectivity constraint and show that the corresponding optimization problems are all NP-hard. For some of these versions we propose two optimization algorithms: (i) a practical heuristic technique which we call DijkstraGC, and (ii) a slow method based on problem decomposition which provides a lower bound on the problem. We use the second technique to verify that for some practical examples DijkstraGC is able to find the global minimum. ©2008 IEEE.</style></abstract></record></records></xml>