ML Anthology

A Hybrid Grammar-Based Approach for Learning and Recognizing Natural Hand Gestures

Amir Sadeghipour, Stefan Kopp
AAAI 2014 pp. 2069-2077
doi:10.1609/AAAI.V28I1.9023 /aaai/2014/sadeghipour2014aaai-hybrid/

Abstract

In this paper, we present a hybrid grammar formalism designed to learn structured models of natural iconic gesture performances that allow for compressed representation and robust recognition. We analyze a dataset of iconic gestures and show how the proposed Feature-based Stochastic Context-Free Grammar (FSCFG) can generalize over both structural and feature-based variations among different gesture performances.

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Cite

Text

Sadeghipour and Kopp. "A Hybrid Grammar-Based Approach for Learning and Recognizing Natural Hand Gestures." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.9023

Markdown

[Sadeghipour and Kopp. "A Hybrid Grammar-Based Approach for Learning and Recognizing Natural Hand Gestures." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/sadeghipour2014aaai-hybrid/) doi:10.1609/AAAI.V28I1.9023

BibTeX

@inproceedings{sadeghipour2014aaai-hybrid,
  title     = {{A Hybrid Grammar-Based Approach for Learning and Recognizing Natural Hand Gestures}},
  author    = {Sadeghipour, Amir and Kopp, Stefan},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2014},
  pages     = {2069-2077},
  doi       = {10.1609/AAAI.V28I1.9023},
  url       = {https://mlanthology.org/aaai/2014/sadeghipour2014aaai-hybrid/}
}