ORASSYLL: Object Recognition with Autonomously Learned and Sparse Symbolic Representations Based on Local Line Detectors
Norbert Krüger, Niklas Lüdtke · 1998
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. 1 Introduction In this paper we describe a novel object recognition system called ORASSYLL (Object Recognition with Autonomously learned and Sparse SYmbolic representations based on Local Line detectors). In ORASSYLL representations of object classes can be learned autonomously. The learned representations are used for a fast and efficient location and identification of objects in complicated scenes. Lea...