TY - GEN
T1 - Identifying objects from hand configurations during in-hand exploration
AU - Faria, Diego R.
AU - Lobo, Jorge
AU - Dias, Jorge
PY - 2012
Y1 - 2012
N2 - In this work we use hand configuration and contact points during in-hand object exploration to identify the manipulated objects. Different contact points associated to an object shape can be represented in a latent space and lie on a lower dimensional non-linear manifold in the contact points space which is suitable for modelling and recognition. Associating and learning hand configurations to specific objects by means of Gaussian mixture models, later by identifying the hand configuration during the in-hand object exploration we can generate hypotheses of candidate objects to be identified. This process selects a set of the most probable objects from a database. The accumulated set of contact points (partial volume of the object shape) during the object in-hand exploration is matched to the set selected from the database (most probable candidate objects). Results are presented for human manipulation of objects, but this can also be applied to artificial hands, although we have not addressed the hand control, only the object identification.
AB - In this work we use hand configuration and contact points during in-hand object exploration to identify the manipulated objects. Different contact points associated to an object shape can be represented in a latent space and lie on a lower dimensional non-linear manifold in the contact points space which is suitable for modelling and recognition. Associating and learning hand configurations to specific objects by means of Gaussian mixture models, later by identifying the hand configuration during the in-hand object exploration we can generate hypotheses of candidate objects to be identified. This process selects a set of the most probable objects from a database. The accumulated set of contact points (partial volume of the object shape) during the object in-hand exploration is matched to the set selected from the database (most probable candidate objects). Results are presented for human manipulation of objects, but this can also be applied to artificial hands, although we have not addressed the hand control, only the object identification.
UR - http://www.scopus.com/inward/record.url?scp=84870621772&partnerID=8YFLogxK
U2 - 10.1109/MFI.2012.6343033
DO - 10.1109/MFI.2012.6343033
M3 - Conference contribution
AN - SCOPUS:84870621772
SN - 9781467325110
T3 - IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
SP - 132
EP - 137
BT - 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2012 - Conference Proceedings
T2 - 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2012
Y2 - 13 September 2012 through 15 September 2012
ER -