no code implementations • ICCV 2019 • Roberto Annunziata, Christos Sagonas, Jacques Cali
Extrapolating fine-grained pixel-level correspondences in a fully unsupervised manner from a large set of misaligned images can benefit several computer vision and graphics problems, e. g. co-segmentation, super-resolution, image edit propagation, structure-from-motion, and 3D reconstruction.
no code implementations • 11 Jul 2018 • Roberto Annunziata, Christos Sagonas, Jacques Calì
In this paper, we propose Densely Fused Spatial Transformer Network (DeSTNet), which, to our best knowledge, is the first dense fusion pattern for combining multiple STNs.
no code implementations • 16 Jan 2018 • Juan Eugenio Iglesias, Marc Modat, Loic Peter, Allison Stevens, Roberto Annunziata, Tom Vercauteren, Ed Lein, Bruce Fischl, Sebastien Ourselin
Here, we overcome this limitation with a probabilistic method that simultaneously solves for registration and synthesis directly on the target images, without any training data.