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NIPS
1998
15 years 7 months ago
Semi-Supervised Support Vector Machines
We introduce a semi-supervised support vector machine (S3 VM) method. Given a training set of labeled data and a working set of unlabeled data, S3 VM constructs a support vector m...
Kristin P. Bennett, Ayhan Demiriz
CVPR
2006
IEEE
16 years 8 months ago
Learning Object Shape: From Drawings to Images
We consider the important challenge of recognizing a variety of deformable object classes in images. Of fundamental importance and particular difficulty in this setting is the pro...
Gal Elidan, Geremy Heitz, Daphne Koller
CVPR
2006
IEEE
16 years 8 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
16 years 8 months ago
Increasing the density of Active Appearance Models
Active Appearance Models (AAMs) typically only use 50-100 mesh vertices because they are usually constructed from a set of training images with the vertices hand-labeled on them. ...
Krishnan Ramnath, Simon Baker, Iain Matthews, Deva...
ECCV
2002
Springer
16 years 8 months ago
On Pencils of Tangent Planes and the Recognition of Smooth 3D Shapes from Silhouettes
This paper presents a geometric approach to recognizing smooth objects from their outlines. We define a signature function that associates feature vectors with objects and baseline...
Svetlana Lazebnik, Amit Sethi, Cordelia Schmid, Da...