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.
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