Abstract
The aim of this paper is to introduce a novel, biologically inspired approach to extract visual features relevant for controlling and understanding reach-to-grasp actions. One of the most relevant of such features has been found to be the grip-size defined as the index finger-tip - thumb-tip distance. For this reason, in this paper we focus on this feature. The human visual system is naturally able to recognize many hand configurations – e.g. gestures or different types of grasps – without being affected substantially by the (observer) viewpoint. The proposed computational model preserves this nice ability.
It is very likely that this ability may play a crucial role in action understanding within primates (and thus human beings). More specifically, a family of neurons in macaque’s ventral premotor area F5 have been discovered which are highly active in correlation with a series of grasp–like movements. This findings triggered a fierce debate about imitation and learning, and inspired several computational models among which the most detailed is due to Oztop and Arbib (MNS model). As a variant of the MNS model, in a previous paper, we proposed the MEP model which relies on an expected perception mechanism. However, both models assume the existence of a mechanism to extract visual features in a viewpoint independent way but neither of them faces the problem of how this mechanism can be achieved in a biologically plausible way. In this paper we propose a neural network model for the extraction of visual features in a viewpoint independent manner, which is based on the work by Poggio and Riesenhuber.
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Prevete, R., Santoro, M., Catanzariti, E., Tessitore, G. (2007). A Neural Network Model for a View Independent Extraction of Reach-to-Grasp Action Features. In: Mele, F., Ramella, G., Santillo, S., Ventriglia, F. (eds) Advances in Brain, Vision, and Artificial Intelligence. BVAI 2007. Lecture Notes in Computer Science, vol 4729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75555-5_12
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DOI: https://doi.org/10.1007/978-3-540-75555-5_12
Publisher Name: Springer, Berlin, Heidelberg
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