<?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%">Rhemann, Christoph</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author><author><style face="normal" font="default" size="100%">Gelautz, Margrit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A spatially varying PSF-based prior for alpha matting</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pages><style face="normal" font="default" size="100%">2149–2156</style></pages><isbn><style face="normal" font="default" size="100%">9781424469840</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper we considerably improve on a state-of-theart alpha matting approach by incorporating a new prior which is based on the image formation process. In particular, we model the prior probability of an alpha matte as the convolution of a high-resolution binary segmentation with the spatially varying point spread function (PSF) of the camera. Our main contribution is a new and efficient deconvolution approach that recovers the prior model, given an approximate alpha matte. By assuming that the PSF is a kernel with a single peak, we are able to recover the binary segmentation with an MRF-based approach, which exploits flux and a new way of enforcing connectivity. The spatially varying PSF is obtained via a partitioning of the image into regions of similar defocus. Incorporating our new prior model into a state-of-the-art matting technique produces results that outperform all competitors, which we confirm using a publicly available benchmark. ©2010 IEEE.</style></abstract></record></records></xml>