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A multichannel convolutional neural network for hand posture recognition
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Link:
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Autor/in:
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Beteiligte Personen:
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Wermter, Stefan
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Weber, Cornelius
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Duch, Włodzisław
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Honkela, Timo
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Koprinkova-Hristova, Petia
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Magg, Sven
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Palm, Günther
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Villa, AlessandroE.P.
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Verlag/Körperschaft:
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Springer International Publishing
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Erscheinungsjahr:
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2014
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Medientyp:
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Text
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Schlagworte:
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Convolution Neural Networks
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Deep Learning
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Hand Postures
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Beschreibung:
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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.
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Lizenz:
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info:eu-repo/semantics/restrictedAccess
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Quellsystem:
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Forschungsinformationssystem der UHH
Interne Metadaten
- Quelldatensatz
- oai:www.edit.fis.uni-hamburg.de:publications/9d2f6802-961d-4adc-8822-32c283ab819e