3D gesture recognition with growing neural gas
Author(s)
Sérra-Perez, JA
García-Rodríguez, J
Orts-Escolano, S
García-Chamizo, JM
Montoyo-Bojo, J
Angelopoulou, A
Psarrou, A
Mentzeopoulos, M
Lewis, A
Griffith University Author(s)
Year published
2013
Metadata
Show full item recordAbstract
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 ...
View more >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.
View less >
View more >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.
View less >
Conference Title
Proceedings of the International Joint Conference on Neural Networks
Subject
Computer vision
Image processing
Virtual and mixed reality