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» Learning Low-Level Vision
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CVPR
2012
IEEE
11 years 11 months ago
Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video
It has recently been shown that only a small number of samples from a low-rank matrix are necessary to reconstruct the entire matrix. We bring this to bear on computer vision prob...
Jun He, Laura Balzano, Arthur Szlam
CVPR
1997
IEEE
14 years 11 months ago
Global Training of Document Processing Systems Using Graph Transformer Networks
We propose a new machine learning paradigm called Graph Transformer Networks that extends the applicability of gradient-based learning algorithms to systems composed of modules th...
Léon Bottou, Yoshua Bengio, Yann LeCun
CVPR
2006
IEEE
14 years 11 months ago
3D People Tracking with Gaussian Process Dynamical Models
We advocate the use of Gaussian Process Dynamical Models (GPDMs) for learning human pose and motion priors for 3D people tracking. A GPDM provides a lowdimensional embedding of hu...
Raquel Urtasun, David J. Fleet, Pascal Fua
CVPR
2008
IEEE
14 years 11 months ago
Face alignment via boosted ranking model
Face alignment seeks to deform a face model to match it with the features of the image of a face by optimizing an appropriate cost function. We propose a new face model that is al...
Gianfranco Doretto, Hao Wu, Xiaoming Liu 0002
ECCV
2004
Springer
14 years 10 months ago
Reliable Fiducial Detection in Natural Scenes
Reliable detection of fiducial targets in real-world images is addressed in this paper. We show that even the best existing schemes are fragile when exposed to other than laborator...
David Claus, Andrew W. Fitzgibbon