A cognitive approach for robots' vision using unsupervised learning and visual saliency

Abstract

In this work we contribute to development of an online unsupervised technique allowing learning of objects from unlabeled images and their detection when seen again. We were inspired by early processing stages of human visual system and by existing work on human infants learning. We suggest a novel fast algorithm for detection of visually salient objects, which is employed to extract objects of interest from images for learning. We demonstrate how this can be used in along with state-of-the-art object recognition algorithms such as SURF and Viola-Jones framework to enable a machine to learn to re-detect previously seen objects in new conditions. We provide results of experiments done on a mobile robot in common office environment with multiple every-day objects. © 2011 Springer-Verlag.