The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
BUILD-IT: An Intuitive Design Tool Based on Direct Object Manipulation
Proceedings of the International Gesture Workshop on Gesture and Sign Language in Human-Computer Interaction
Special Topics of Gesture Recognition Applied in Intelligent Home Environments
Proceedings of the International Gesture Workshop on Gesture and Sign Language in Human-Computer Interaction
High Performance Real-Time Gesture Recognition Using Hidden Markov Models
Proceedings of the International Gesture Workshop on Gesture and Sign Language in Human-Computer Interaction
Neural Architecture for Gesture-Based Human-Machine-Interaction
Proceedings of the International Gesture Workshop on Gesture and Sign Language in Human-Computer Interaction
Ein Interaktives Mobiles Service-System für den Baumarkt
Autonome Mobile Systeme 1999, 15. Fachgespräch
User Localisation for Visually-Based Human-Machine-Interaction
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Recognizing and interpreting gestures on a mobile robot
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Real-Time Gesture Recognition by Means of Hybrid Recognizers
GW '01 Revised Papers from the International Gesture Workshop on Gesture and Sign Languages in Human-Computer Interaction
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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.