TY  - BOOK
AB  - In human-machine interaction scenarios, low latency recognition and reproduction is crucial for successful communication. For reproduction of general gesture classes it is important to realize a representation that is insensitive with respect to the variation of performer specific speed development along gesture trajectories. Here, we present an approach to learning of speed-invariant gesture models that provide fast recognition and convenient reproduction of gesture trajectories. We evaluate our gesture model with a data set comprising 520 examples for 48 gesture classes. The results indicate that the model is able to learn gestures from few observations with high accuracy.
DA  - 2012
LA  - eng
PY  - 2012
TI  - Low Latency Recognition and Reproduction of Natural Gesture Trajectories
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-24564457
Y2  - 2024-11-22T04:13:49
ER  -