Abstract
The development of a hybrid system for (mainly) gesturebased 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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Boehme, HJ., Braumann, UD., Corradini, A., Gross, HM. (1999). Person Localization and Posture Recognition for Human-Robot Interaction. In: Braffort, A., Gherbi, R., Gibet, S., Teil, D., Richardson, J. (eds) Gesture-Based Communication in Human-Computer Interaction. GW 1999. Lecture Notes in Computer Science(), vol 1739. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46616-9_11
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DOI: https://doi.org/10.1007/3-540-46616-9_11
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