Data set for object recognition and categorization. 10 categories, 40 objects for the training phase. The acquisition size is 640×480 and subsequently cropped to the bounding box of the object according to the kinematics or motion cue. The bounding box is 160×160 in human mode and 320×320 in robot mode. For each object we provide 200 training samples. Each category is trained with 3 objects (600 examples per category).

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Publications

Fanello, S.R.; Ciliberto, C.; Santoro, M.; Natale, L.; Metta, G.; Rosasco, L.; Odone, F.,”iCub World: Friendly Robots Help Building Good Vision Data-Sets,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR), 2013

Fanello, S. R.; Ciliberto, C.; Natale, L.; Metta, G., “Weakly Supervised Strategies for Natural Object Recognition in Robotics,” IEEE International Conference on Robotics and Automation (ICRA). Karlsruhe, Germany, May 6-10, 2013

Fanello, S.R.; Noceti, N.; Metta, G.; Odone, F., “Multi-Class Image Classification: Sparsity Does It Better,” International Conference on Computer Vision Theory and Applications (VISAPP), 2013

Ciliberto C.; Smeraldi F.; Natale L.; Metta G., “Online Multiple Instance Learning Applied to Hand Detection in a Humanoid Robot,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2011). San Francisco, California, USA, September 25-30, 2011