Generating motion trajectories by sparse activation of learned motion primitives

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7552 LNCS, Issue PART 1, 2012, Pages 637-644

  Vollmer, Christian ^a   Eggert, Julian P ^b   Gross, Horst Michael ^a  

^a ILMENAU UNIVERSITY OF TECHNOLOGY   (Germany)

^b HONDA RESEARCH INSTITUTE EUROPE GMBH   (Germany)

Author keywords

motion primitives; non negative matrix factorization; spiking neurons

Indexed keywords

BIOLOGICAL MOTION; COMPACT REPRESENTATION; GENERATIVE MODEL; HAND-WRITTEN CHARACTERS; INTEGRATE-AND-FIRE NEURONS; LARGE DATASETS; MOTION PRIMITIVES; MOTION TRAJECTORIES; NONNEGATIVE MATRIX FACTORIZATION; SPIKING NEURON; SUB-BLOCKS; TIMING INFORMATION; TWO LAYER MODEL;

FACTORIZATION; NEURONS; STOCHASTIC MODELS; TRAJECTORIES;

NEURAL NETWORKS;

EID: 84867665370     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33269-2_80     Document Type: Conference Paper

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