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Proceedings of the International Conference on Computer Vision Theory and Applications
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 featuresdoi:10.5220/0003312802350241 fatcat:tatvazbxxjboldylrdtdnuswga