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» Lagrangian Relaxation for MAP Estimation in Graphical Models
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ICML
2001
IEEE
14 years 8 months ago
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hid...
John D. Lafferty, Andrew McCallum, Fernando C. N. ...
IJCV
2006
171views more  IJCV 2006»
13 years 7 months ago
Combining Generative and Discriminative Models in a Framework for Articulated Pose Estimation
We develop a method for the estimation of articulated pose, such as that of the human body or the human hand, from a single (monocular) image. Pose estimation is formulated as a s...
Rómer Rosales, Stan Sclaroff
CG
2008
Springer
13 years 7 months ago
Masked photo blending: Mapping dense photographic data set on high-resolution sampled 3D models
The technological advance of sensors is producing an exponential size growth of the data coming from 3D scanning and digital photography. The production of digital 3D models consi...
Marco Callieri, Paolo Cignoni, Massimiliano Corsin...
CGF
2010
199views more  CGF 2010»
13 years 6 months ago
Time-of-Flight Cameras in Computer Graphics
A growing number of applications depend on accurate and fast 3D scene analysis. Examples are model and lightfield acquisition, collision prevention, mixed reality, and gesture re...
Andreas Kolb, Erhardt Barth, Reinhard Koch, Rasmus...
DAGM
2009
Springer
14 years 7 days ago
Video Super Resolution Using Duality Based TV-L1 Optical Flow
Abstract. In this paper, we propose a variational framework for computing a superresolved image of a scene from an arbitrary input video. To this end, we employ a recently proposed...
Dennis Mitzel, Thomas Pock, Thomas Schoenemann, Da...