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2010
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14 years 11 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...
CVPR
2006
IEEE
16 years 7 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
ICPR
2008
IEEE
16 years 6 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...
ICPR
2008
IEEE
15 years 11 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
ICPR
2008
IEEE
15 years 11 months ago
Tracking human body by using particle filter Gaussian process Markov-switching model
The goal of this article is to present an effective and robust tracking algorithm for nonlinear feet motion by deploying particle filter integrated with Gaussian process latent v...
Jing Wang, Hong Man, Yafeng Yin