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 • CVPR 2017 • Christos Sagonas, Yannis Panagakis, Alina Leidinger, Stefanos Zafeiriou
Even though the CCA is a powerful tool, it has several drawbacks that render its application challenging for computer vision applications.
no code implementations • ICCV 2015 • Christos Sagonas, Yannis Panagakis, Stefanos Zafeiriou, Maja Pantic
The proposed method is assessed in frontal face reconstruction, face landmark localization, pose-invariant face recognition, and face verification in unconstrained conditions.
no code implementations • 3 Feb 2015 • Christos Sagonas, Yannis Panagakis, Stefanos Zafeiriou, Maja Pantic
The proposed method is assessed in frontal face reconstruction (pose correction), face landmark localization, and pose-invariant face recognition and verification by conducting experiments on $6$ facial images databases.
no code implementations • CVPR 2014 • Christos Sagonas, Yannis Panagakis, Stefanos Zafeiriou, Maja Pantic
Next, to correct the fittings of a generic model, image congealing (i. e., batch image aliment) is performed by employing only the learnt orthonormal subspace.