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  Visual Gesture Recognition by a Modular Neural System [1 citations — 0 self]

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by Enno Littmann, Andrea Drees, Helge Ritter
International ConferenceonArti Neural Networks,Bochum
ftp://neuro.informatik.uni-ulm.de/ni/enno/littmann.icann96.ps.gz
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Abstract:

Abstract. The visual recognition of human hand pointing gestures from stereo pairs of video camera images provides a very intuitive kind of man-machine interface. We show that a modular, neural network based system can solve this task in a realistic laboratory environment. Several neural networks account for image segmentation, estimation of hand location, estimation of 3D-pointing direction, and necessary transforms from image to world coordinates and vice versa. The functions of all network modules can be learned from data examples only, by exploiting various learning algorithms. We investigate the performance of such a system and dicuss the problem of operator-independent recognition. 1

Citations

57 A Hand Gesture Interface Device – Zimmerman, Lanier, et al. - 1987
22 Gesture Driven Interaction as a Human Factor in Virtual Environments - An Approach with Neural Networks – Vaananen, Bohm - 1993
19 Recognizing hand gestures – Davis, Shah - 1994
17 A convolutional neural network hand tracker – Nowlan, Platt - 1995
16 Learning 3D-shape-perception with local linear maps – Meyering, Ritter - 1992
13 Classifying hand gestures with a view-based distributed representation – Darrell, Pentland - 1994
7 Neural and statistical methods for adaptive color segmentation --- a comparison – Littmann, Ritter - 1995
6 Recognition of 3d-hand orientation from monocular color images by neural semantic networks – Kummert, Littmann, et al. - 1993
5 A novel device for using the hand as a human-computer interface – Maggioni - 1993
5 Learning 3D hand postures from perspective pixel images – Meyering, Ritter - 1992
3 Visual gesture-based robot guidance with a modular neural system – Littmann, Drees, et al. - 1996