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» Learning for Optical Flow Using Stochastic Optimization
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ICIP
2010
IEEE
13 years 5 months ago
Randomized motion estimation
Motion estimation is known to be a non-convex optimization problem. This non-convexity comes from several ambiguities in motion estimation such as the aperture problem, or fast mo...
Sylvain Boltz, Frank Nielsen
ECAI
2010
Springer
13 years 8 months ago
Bayesian Monte Carlo for the Global Optimization of Expensive Functions
In the last decades enormous advances have been made possible for modelling complex (physical) systems by mathematical equations and computer algorithms. To deal with very long run...
Perry Groot, Adriana Birlutiu, Tom Heskes
ATAL
2004
Springer
14 years 1 months ago
Adaptive, Distributed Control of Constrained Multi-Agent Systems
Product Distribution (PD) theory was recently developed as a framework for analyzing and optimizing distributed systems. In this paper we demonstrate its use for adaptive distribu...
Stefan Bieniawski, David Wolpert
ICIP
2006
IEEE
14 years 9 months ago
Two-Stage Optimal Component Analysis
Linear techniques are widely used to reduce the dimension of image representation spaces in applications such as image indexing and object recognition. Optimal Component Analysis ...
Yiming Wu, Xiuwen Liu, Washington Mio, Kyle A. Gal...
GECCO
2006
Springer
185views Optimization» more  GECCO 2006»
13 years 11 months ago
Robot gaits evolved by combining genetic algorithms and binary hill climbing
In this paper an evolutionary algorithm is used for evolving gaits in a walking biped robot controller. The focus is fast learning in a real-time environment. An incremental appro...
Lena Mariann Garder, Mats Erling Høvin