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» Creating Generative Models from Range Images
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IIS
2000
15 years 7 months ago
Speeding Up Evolution through Learning: LEM
This paper reports briefly on the development of a new approach to evolutionary computation, called the Learnable Evolution Model or LEM. In contrast to conventional Darwinian-typ...
Ryszard S. Michalski, Guido Cervone, Kenneth A. Ka...
TROB
2010
159views more  TROB 2010»
15 years 1 months ago
Task-Specific Generalization of Discrete and Periodic Dynamic Movement Primitives
Abstract--Acquisition of new sensorimotor knowledge by imitation is a promising paradigm for robot learning. To be effective, action learning should not be limited to direct replic...
Ales Ude, Andrej Gams, Tamim Asfour, Jun Morimoto
CORR
2011
Springer
222views Education» more  CORR 2011»
14 years 10 months ago
Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs
Abstract. We propose an extension of the Restricted Boltzmann Machine (RBM) that allows the joint shape and appearance of foreground objects in cluttered images to be modeled indep...
Nicolas Heess, Nicolas Le Roux, John M. Winn
AFRIGRAPH
2001
ACM
15 years 10 months ago
Expressive textures
If a moving image is more expressive than words or than a still image, then an animated facial expression can explain more in depth the feelings of a virtual character. Facial ani...
K. Fei
MIA
2010
170views more  MIA 2010»
15 years 1 months ago
Linear intensity-based image registration by Markov random fields and discrete optimization
We propose a framework for intensity-based registration of images by linear transformations, based on a discrete Markov Random Field (MRF) formulation. Here, the challenge arises ...
Darko Zikic, Ben Glocker, Oliver Kutter, Martin Gr...