<?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%">Márquez-Neila, Pablo</style></author><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Baumela, Luis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Non-parametric higher-order random fields for image segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">biomedical image analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">higher-order models</style></keyword><keyword><style  face="normal" font="default" size="100%">image denoising</style></keyword><keyword><style  face="normal" font="default" size="100%">Image segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">random fields</style></keyword><keyword><style  face="normal" font="default" size="100%">structured prediction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><number><style face="normal" font="default" size="100%">PART 6</style></number><volume><style face="normal" font="default" size="100%">8694 LNCS</style></volume><pages><style face="normal" font="default" size="100%">269–284</style></pages><isbn><style face="normal" font="default" size="100%">9783319105987</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Models defined using higher-order potentials are becoming increasingly popular in computer vision. However, the exact representation of a general higher-order potential defined over many variables is computationally unfeasible. This has led prior works to adopt parametric potentials that can be compactly represented. This paper proposes a non-parametric higher-order model for image labeling problems that uses a patch-based representation of its potentials. We use the transformation scheme of [11, 25] to convert the higher-order potentials to a pair-wise form that can be handled using traditional inference algorithms. This representation is able to capture structure, geometrical and topological information of labels from training data and to provide more precise segmentations. Other tasks such as image denoising and reconstruction are also possible. We evaluate our method on denoising and segmentation problems with synthetic and real images. © 2014 Springer International Publishing.</style></abstract></record></records></xml>