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» Learning Low-Level Vision
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BMVC
2010
13 years 6 months ago
Back to the Future: Learning Shape Models from 3D CAD Data
Recognizing 3D objects from arbitrary view points is one of the most fundamental problems in computer vision. A major challenge lies in the transition between the 3D geometry of o...
Michael Stark, Michael Goesele, Bernt Schiele
ICCV
2011
IEEE
12 years 8 months ago
Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
George Papandreou, Alan L. Yuille
ICPR
2008
IEEE
14 years 3 months ago
Semi-supervised discriminant analysis based on UDP regularization
We propose a semi-supervised learning algorithm for discriminant analysis, which uses the geometric structure of both labeled and unlabeled samples and perform a manifold regulari...
Huining Qiu, Jian-Huang Lai, Jian Huang, Yu Chen
CVPR
2005
IEEE
14 years 10 months ago
Learning to Track: Conceptual Manifold Map for Closed-Form Tracking
Our objective is to model the visual manifold of object appearance corresponding to geometric transformation. We learn a generative model for object appearance where the appearanc...
Ahmed M. Elgammal
ECCV
2006
Springer
14 years 10 months ago
Tracking Objects Across Cameras by Incrementally Learning Inter-camera Colour Calibration and Patterns of Activity
This paper presents a scalable solution to the problem of tracking objects across spatially separated, uncalibrated, non-overlapping cameras. Unlike other approaches this technique...
Andrew Gilbert, Richard Bowden