<?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%">Gehler, Peter Vincent</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kiefel, Martin</style></author><author><style face="normal" font="default" size="100%">Zhang, Lumin</style></author><author><style face="normal" font="default" size="100%">Schölkopf, Bernhard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Recovering intrinsic images with a global sparsity prior on reflectance</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><isbn><style face="normal" font="default" size="100%">9781618395993</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We address the challenging task of decoupling material properties from lighting properties given a single image. In the last two decades virtually all works have concentrated on exploiting edge information to address this problem. We take a different route by introducing a new prior on reflectance, that models reflectance values as being drawn from a sparse set of basis colors. This results in a Random Field model with global, latent variables (basis colors) and pixel-accurate output reflectance values. We show that without edge information high-quality results can be achieved, that are on par with methods exploiting this source of information. Finally, we are able to improve on state-of-the-art results by integrating edge information into our model. We believe that our new approach is an excellent starting point for future developments in this field.</style></abstract></record></records></xml>