loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Giovanni Tessitore ; Francesco Donnarumma and Roberto Prevete

Affiliation: University of Naples Federico II, Italy

ISBN: 978-989-8425-32-4

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 mor e 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)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.204.48.199

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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 - Volume 1: ICNC, (IJCCI 2010) ISBN 978-989-8425-32-4, 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 - Volume 1: ICNC, (IJCCI 2010)},
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 - Volume 1: ICNC, (IJCCI 2010)
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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.