A multichannel convolutional neural network for hand posture recognition

Link:
Autor/in:
Beteiligte Personen:
  • Wermter, Stefan
  • Weber, Cornelius
  • Duch, Włodzisław
  • Honkela, Timo
  • Koprinkova-Hristova, Petia
  • Magg, Sven
  • Palm, Günther
  • Villa, AlessandroE.P.
Verlag/Körperschaft:
Springer International Publishing
Erscheinungsjahr:
2014
Medientyp:
Text
Schlagworte:
  • Convolution Neural Networks
  • Deep Learning
  • Hand Postures
Beschreibung:
  • Natural communication between humans involves hand gestures, which has an impact on research in human-robot interaction. In a real-world scenario, understanding human gestures by a robot is hard due to several challenges like hand segmentation. To recognize hand postures this paper proposes a novel convolutional implementation. The model is able to recognize hand postures recorded by a robot camera in real-time, in a real-world application scenario. The proposed model was also evaluated with a benchmark database and showed better results than the ones reported in the benchmark paper. © 2014 Springer International Publishing Switzerland.
Lizenz:
  • info:eu-repo/semantics/restrictedAccess
Quellsystem:
Forschungsinformationssystem der UHH
Interne Metadaten
Quelldatensatz
oai:www.edit.fis.uni-hamburg.de:publications/9d2f6802-961d-4adc-8822-32c283ab819e