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Authors: Giovanni Tessitore ; Francesco Donnarumma and Roberto Prevete

Affiliation: University of Naples Federico II, Italy

Keyword(s): Neural networks, Grasping action, Hand pose estimation, Mixture density networks.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Complex Artificial Neural Network Based Systems and Dynamics ; Computational Intelligence ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image Processing ; Informatics in Control, Automation and Robotics ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Robotics and Automation ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: There is a growing interest in developing computational models of grasping action recognition. This interest is increasingly motivated by a wide range of applications in robotics, neuroscience, HCI, motion capture and other research areas. In many cases, a vision-based approach to grasping action recognition appears to be more promising. For example, in HCI and robotic applications, such an approach often allows for simpler and more natural interaction. However, a vision-based approach to grasping action recognition is a challenging problem due to the large number of hand self-occlusions which make the mapping from hand visual appearance to the hand pose an inverse ill-posed problem. The approach proposed here builds on the work of Santello and co-workers which demonstrate a reduction in hand variability within a given class of grasping actions. The proposed neural network architecture introduces specialized modules for each class of grasping actions and viewpoints, allowing for a mo re robust hand pose estimation. A quantitative analysis of the proposed architecture obtained by working on a synthetic data set is presented and discussed as a basis for further work. (More)

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Paper citation in several formats:
Tessitore, G.; Donnarumma, F. and Prevete, R. (2010). AN ACTION-TUNED NEURAL NETWORK ARCHITECTURE FOR HAND POSE ESTIMATION. In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation (IJCCI 2010) - ICNC; ISBN 978-989-8425-32-4, SciTePress, pages 358-363. DOI: 10.5220/0003086403580363

@conference{icnc10,
author={Giovanni Tessitore. and Francesco Donnarumma. and Roberto Prevete.},
title={AN ACTION-TUNED NEURAL NETWORK ARCHITECTURE FOR HAND POSE ESTIMATION},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation (IJCCI 2010) - ICNC},
year={2010},
pages={358-363},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003086403580363},
isbn={978-989-8425-32-4},
}

TY - CONF

JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation (IJCCI 2010) - ICNC
TI - AN ACTION-TUNED NEURAL NETWORK ARCHITECTURE FOR HAND POSE ESTIMATION
SN - 978-989-8425-32-4
AU - Tessitore, G.
AU - Donnarumma, F.
AU - Prevete, R.
PY - 2010
SP - 358
EP - 363
DO - 10.5220/0003086403580363
PB - SciTePress