2011 Proceedings of the International Conference on Computer Vision Theory and Applications   unpublished
We present a Bag of words-based active object categorization technique implemented and tested on a humanoid robot. The robot is trained to categorize objects that are handed to it by a human operator. The robot uses hand and head motions to actively acquire a number of different views. A view planning scheme using entropy minimization reduces the number of views needed to achieve a valid decision. Categorization results are significantly improved by active elimination of background features
more » ... g robot arm motion. Our experiments cover both, categorization when the object is handed to the robot in a fixed pose at training and testing, and object pose independent categorization. Results on a 4-class object database demonstrate the classification efficiency, a significant gain from multi-view compared to single-view classification, and the advantage of view planning. We conclude that humanoid robotic systems can be successfully applied to actively categorize objects -a task with many potential applications ranging from edutainment to active surveillance.
doi:10.5220/0003312802350241 fatcat:tatvazbxxjboldylrdtdnuswga