Paper Info
Title
3D gesture recognition with growing neural gas.
Abstract
We propose the design of a real-time system to recognize and interpret hand gestures. The acquisition devices are low cost 3D sensors. 3D hand pose segmentation, characterization and tracking will be implemented using the growing neural gas (GNG) structure. The capacity of the system to obtain information with a high degree of freedom allows the encoding of many gestures and a very accurate motion capture. The use of hand pose models combined with motion information provided with GNG permits to deal with the problem of the hand motion representation. A natural interface applied to a virtual mirror writing system and a module to estimate hand pose have been designed to demonstrate the validity of the system.
Year
DOI
Venue
2013
10.1109/IJCNN.2013.6707129
IJCNN
Keywords
Field
DocType
gesture recognition,human computer interaction,image motion analysis,image representation,image segmentation,image sensors,neural nets,object tracking,pose estimation,3D hand gesture recognition,3D hand pose characterization,3D hand pose segmentation,3D hand pose tracking,3D sensors,GNG,acquisition devices,degree-of-freedom,gesture encoding,growing neural gas structure,hand gesture interpretation,hand motion representation,hand pose estimation,motion capturing,natural interface,real-time system design,virtual mirror writing system
Motion capture,Computer vision,Pattern recognition,Computer science,Gesture,Gesture recognition,Image segmentation,Pose,Video tracking,Artificial intelligence,Natural user interface,Neural gas
Conference
ISSN
Citations 
PageRank 
2161-4393
0
0.34
References 
Authors
13
9
Name
Order
Citations
PageRank
Jose Antonio Serra-Perez140.73
José García Rodríguez219229.10
Sergio Orts-Escolano331329.45
Juan Manuel García-Chamizo4728.98
Javier Montoyo-Bojo500.68
Anastassia Angelopoulou610221.29
Alexandra Psarrou719927.14
Markos Mentzelopoulos83910.45
Andrew Lewis9154.30