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» A Guided Monte Carlo Approach to Optimization Problems
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IBPRIA
2005
Springer
14 years 28 days ago
Monte Carlo Localization Using SIFT Features
The ability of finding its situation in a given environment is crucial for an autonomous agent. While navigating through a space, a mobile robot must be capable of finding its lo...
Arturo Gil, Óscar Reinoso, Maria Asunci&oac...
AAAI
2006
13 years 8 months ago
Bayesian Calibration for Monte Carlo Localization
Localization is a fundamental challenge for autonomous robotics. Although accurate and efficient techniques now exist for solving this problem, they require explicit probabilistic...
Armita Kaboli, Michael H. Bowling, Petr Musí...
WSC
2008
13 years 9 months ago
A particle filtering framework for randomized optimization algorithms
We propose a framework for optimization problems based on particle filtering (also called Sequential Monte Carlo method). This framework unifies and provides new insight into rand...
Enlu Zhou, Michael C. Fu, Steven I. Marcus
IJON
2006
131views more  IJON 2006»
13 years 7 months ago
Optimizing blind source separation with guided genetic algorithms
This paper proposes a novel method for blindly separating unobservable independent component (IC) signals based on the use of a genetic algorithm. It is intended for its applicati...
J. M. Górriz, Carlos García Puntonet...
CVIU
2006
176views more  CVIU 2006»
13 years 7 months ago
Temporal motion models for monocular and multiview 3D human body tracking
We explore an approach to 3D people tracking with learned motion models and deterministic optimization. The tracking problem is formulated as the minimization of a differentiable ...
Raquel Urtasun, David J. Fleet, Pascal Fua