PUB - Publikationen an der Universität Bielefeld

Browse > Journals> Neural Networks, IEEE Transact ...> Volume: 13 Issue: 4 Visual recognition of continuous hand postures * 1021898 abstract Download Citations * Email * Print * Rights And Permissions * Access The Full Text Sign In:Full text access may be available with your subscription User Name Password Forgot Username/Password? Athens/Shibboleth Sign In * Already Purchased * Purchase Now Nolker, C.; Ritter, H.; Neuroinformatics Dept., Bielefeld Univ. This paper appears in: Neural Networks, IEEE Transactions on Issue Date: Jul 2002 Volume: 13 Issue:4 On page(s): 983 - 994 ISSN: 1045-9227 References Cited: 27 Cited by : 6 INSPEC Accession Number: 7353776 Digital Object Identifier: 10.1109/TNN.2002.1021898 Date of Current Version: 07 November 2002 Sponsored by: IEEE Computational Intelligence Society Abstract This paper describes GREFIT (Gesture REcognition based on FInger Tips), a neural network-based system which recognizes continuous hand postures from gray-level video images (posture capturing). Our approach yields a full identification of all finger joint angles (making, however, some assumptions about joint couplings to simplify computations). This allows a full reconstruction of the three-dimensional (3-D) hand shape, using an articulated hand model with 16 segments and 20 joint angles. GREFIT uses a two-stage approach to solve this task. In the first stage, a hierarchical system of artificial neural networks (ANNs) combined with a priori knowledge locates the two-dimensional (2-D) positions of the finger tips in the image. In the second stage, the 2-D position information is transformed by an ANN into an estimate of the 3-D configuration of an articulated hand model, which is also used for visualization. This model is designed according to the dimensions and movement possibilities of a natural human hand. The virtual hand imitates the user's hand to an remarkable accuracy and can follow postures from gray scale images at a frame rate of 10 Hz.