TY - GEN
T1 - Developmental approach for interactive object discovery
AU - Lyubova, Natalia
AU - Filliat, David
PY - 2012/8/22
Y1 - 2012/8/22
N2 - We present a visual system for a humanoid robot that supports an efficient online learning and recognition of various elements of the environment. Taking inspiration from child's perception and following the principles of developmental robotics, our algorithm does not require image databases, predefined objects nor face/skin detectors. The robot explores the visual space from interactions with people and its own experiments. The object detection is based on the hypothesis of coherent motion and appearance during manipulations. A hierarchical object representation is constructed from SURF points and color of superpixels that are grouped in local geometric structures and form the basis of a multiple-view object model. The learning algorithm accumulates the statistics of feature occurrences and identifies objects using a maximum likelihood approach and temporal coherency. The proposed visual system is implemented on the iCub robot and shows 85% average recognition rate for 10 objects after 30 minutes of interaction.
AB - We present a visual system for a humanoid robot that supports an efficient online learning and recognition of various elements of the environment. Taking inspiration from child's perception and following the principles of developmental robotics, our algorithm does not require image databases, predefined objects nor face/skin detectors. The robot explores the visual space from interactions with people and its own experiments. The object detection is based on the hypothesis of coherent motion and appearance during manipulations. A hierarchical object representation is constructed from SURF points and color of superpixels that are grouped in local geometric structures and form the basis of a multiple-view object model. The learning algorithm accumulates the statistics of feature occurrences and identifies objects using a maximum likelihood approach and temporal coherency. The proposed visual system is implemented on the iCub robot and shows 85% average recognition rate for 10 objects after 30 minutes of interaction.
UR - https://www.scopus.com/pages/publications/84865090273
U2 - 10.1109/IJCNN.2012.6252606
DO - 10.1109/IJCNN.2012.6252606
M3 - Conference contribution
AN - SCOPUS:84865090273
SN - 9781467314909
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2012 International Joint Conference on Neural Networks, IJCNN 2012
T2 - 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
Y2 - 10 June 2012 through 15 June 2012
ER -