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Calligraphic Stylisation Learning with a Physiologically Pl..:

, In: Proceedings of the 4th International Conference on Movement Computing,

Berio, Daniel ; Akten, Memo ; Leymarie, Frederic Fol.. - p. 1-8 , 2017

Link: https://dl.acm.org/doi/10.1145/3077981.3078049

RT 
T1 <cTitle>Proceedings of the 4th International Conference on Movement Computing</cTitle>: T1 <Title>Calligraphic Stylisation Learning with a Physiologically Plausible Model of Movement and Recurrent Neural Networks</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3078049&Exemplar=1&LAN=DE
				
A1 Berio, Daniel
A1 Akten, Memo
A1 Leymarie, Frederic Fol
A1 Grierson, Mick
A1 Plamondon, Réjean
PB ACM
YR 2017
K1 Human hand-writing movement modeling
K1 LSTM
K1 Long Short-Term Memory architecture
K1 MDN
K1 Mixture Density Network
K1 RMDN
K1 RNN
K1 Recurrent Mixture Density Network
K1 Recurrent Neural Network
K1 curve and path dynamics
K1 graffiti tags
K1 graphonomics
K1 one-shot learning
K1 procedural calligraphic and graffiti production
K1 Computing methodologies
K1 Machine learning
K1 Machine learning approaches
K1 Neural networks
K1 Human-centered computing
K1 Human computer interaction (HCI)
K1 Interaction techniques
K1 Gestural input
K1 Computer graphics
K1 Animation
K1 Procedural animation
SP 1
OP 8
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3077981.3078049
DO https://dl.acm.org/doi/10.1145/3077981.3078049
SF ELIB - SuUB Bremen

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