Human motion assessment in real time using recurrent self-organization
By German Ignacio Parisi, Sven Magg, Stefan Wermter. 2016
55 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- German Ignacio Parisi
- Stefan Wermter
- Luiza Mici
- Cornelius Weber
- Jun Tani
- Pablo V. A. Barros
- Vincenzo Lomonaco
- Christopher Kanan
In Computer Vision and Pattern Recognition
- German Ignacio Parisi
- Stefan Wermter
- Luiza Mici
- Jun Tani
- Cornelius Weber
- Pablo V. A. Barros
- Nourhan Elfaramawy
- Vincenzo Lomonaco
In Signal Processing
Citations in the corpus
- An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction (2017, methodology): “The application of a growing self-organizing network, such as the GWR, allows for the learning of prototypical motion patterns in an incremental fashion [39].”
- Lifelong learning of human actions with deep neural network self-organization (2017, methodology): “For K = 1, i.e. only one context descriptor, the GammaGWR is reduced to the temporal GWR as introduced by Parisi et al. (2016).”
- Emotion Recognition from Body Expressions with a Neural Network Architecture (2017, background): “7 ] and decreasing values of α i yield minimal temporal quantization error [18].”