The development of a hybrid system for (mainly) gesture-based human-robot interaction is presented, thereby describing the progress in comparison to the work shown at the last gesture workshop (see [2]). The system makes use of standard image processing techniques as well as of neural information processing. The performance of our architecture includes the detection of a person as a potential user in an indoor environment, followed by the recognition of her gestural instructions. In this paper, we concentrate on two major mechanisms: (ⅰ), the contour-based person localization via a combination of steerable filters and three-dimensional dynamic neural fields, and (ⅱ), our first experiences concerning the recognition of different instructional postures via a combination of statistical moments and neural classifiers.
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