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112
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SAMOS
2010
Springer
15 years 1 months ago
Identifying communication models in Process Networks derived from Weakly Dynamic Programs
—Process Networks (PNs) is an appealing computation ion helping to specify an application in parallel form and realize it on parallel platforms. The key questions to be answered ...
Dmitry Nadezhkin, Todor Stefanov
119
Voted
TIP
2010
164views more  TIP 2010»
14 years 9 months ago
A Marked Point Process for Modeling Lidar Waveforms
Lidar waveforms are 1D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful ...
Clément Mallet, Florent Lafarge, Michel Rou...
144
Voted
CVPR
2006
IEEE
16 years 4 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
102
Voted
ICPR
2008
IEEE
16 years 3 months ago
Detection of digital processing of images through a realistic model of CCD noise
In this paper, we propose a method for detecting digital processing of video such as compositing. Our method is based on a realistic model of charge coupled device (CCD) sensor no...
Jean-Baptiste Maillard, Daniel Lévesque, Fr...
123
Voted
ICPR
2008
IEEE
15 years 9 months ago
Manifold denoising with Gaussian Process Latent Variable Models
For a finite set of points lying on a lower dimensional manifold embedded in a high-dimensional data space, algorithms have been developed to study the manifold structure. Howeve...
Yan Gao, Kap Luk Chan, Wei-Yun Yau