3D object recognition based on a geometrical topology model and extreme learning machine

Neural Computing and Applications

Volumn 22, Issue 3-4, 2013, Pages 427-433

  Nian, Rui ^a   He, Bo ^a   Lendasse, Amaury ^b,c,d  

^a OCEAN UNIVERSITY OF CHINA   (China)

^b AALTO UNIVERSITY   (Finland)

^c BASQUE FOUNDATION FOR SCIENCE   (Spain)

^d UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

Dipole topology; Extreme learning machines; Geometrical topology hypothesis; Optimal cognition principle

Indexed keywords

FEEDFORWARD NEURAL NETWORKS; GEOMETRY; KNOWLEDGE ACQUISITION; MACHINE LEARNING; MULTILAYER NEURAL NETWORKS; NETWORK LAYERS; OBJECT RECOGNITION; THREE DIMENSIONAL COMPUTER GRAPHICS;

3D OBJECT RECOGNITION; DEPENDENCE STRUCTURES; ELM ALGORITHMS; EXTREME LEARNING MACHINE; GEOMETRICAL TOPOLOGY; MULTIPLE VIEWS; OPTIMAL COGNITION PRINCIPLE; SINGLE-HIDDEN LAYER FEEDFORWARD NEURAL NETWORKS;

TOPOLOGY;

EID: 84874017920     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-012-0892-7     Document Type: Article

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