We introduce an object recognition 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. Learning can be performed autonomously by utilizing motor-controlled feedback. The learned representation are used for fast and efficient localization and discrimination of objects in complex scenes.
