ORASSYLL: Object Recognition with Autonomously Learned and Sparse Symbolic Representations Based on Metrically Organized Local Line Detectors

We introduce an object recognition and localization system in which objects are represented as a sparse and spatially organized set of local (bent) line segments. The line segments correspond to binarized Gabor wavelets or banana wavelets, which are bent and stretched Gabor wavelets. These features can be metrically organized; the metric enables an efficient learning of object representations. It is essential for learning that only corresponding local areas are compared with each other; i.e., the correspondence problem has to be solved. We achieve correpondence (and in this way autonomous learning) by utilizing motor-controlled feedback, i.e., by interaction of arm movement and camera tracking. The learned representations are used for fast and efficient localization and discrimination of objects in complex scenes.