@article {385, title = {Object recognition data sets (iCub/IIT)}, year = {2013}, month = {05/2013}, abstract = {

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

Click HERE to Download Dataset from IIT website \>

Publications

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

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

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

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

}, keywords = {Computer vision, object recognition, robotics}, author = {Lorenzo Rosasco} }