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There is an ongoing debate over the capabilities of hierarchical neural feedforward architectures for performing real-world invariant object recognition. Although a variety of hierarchical models exists, appropriate supervised and unsupervised learning methods are still an issue of intense research. We propose a feedforward model for recognition that shares(More)
This paper proposes a biologically inspired and technically implemented sound localization system to robustly estimate the position of a sound source in the frontal azimuthal half-plane. For localization, binaural cues are extracted using cochleagrams generated by a cochlear model that serve as input to the system. The basic idea of the model is to(More)
Invariant object recognition is arguably one of the major challenges for contemporary machine vision systems. In contrast, the mammalian visual system performs this task virtually effortlessly. How can we exploit our knowledge on the biological system to improve artificial systems? Our understanding of the mammalian early visual system has been augmented by(More)
We present an approach for the supervised online learning of object representations based on a biologically motivated architecture of visual processing. We use the output of a recently developed topograph-ical feature hierarchy to provide a view-based representation of three-dimensional objects using a dynamical vector quantization approach. For a simple(More)
We present a biologically motivated architecture for object recognition that is based on a hierarchical feature-detection model in combination with a memory architecture that implements short-term and long-term memory for objects. A particular focus is the functional realization of online and incremental learning for the task of appearance-based object(More)
We address the problem of fast figure-ground segmentation of single objects from cluttered backgrounds to improve object learning and recognition. For the segmen-tation, we use an initial foreground hypothesis to train a classifier for figure and ground on topographically ordered feature maps with Generalized Learning Vector Quantization. We investigate the(More)
We present a new method capable of learning multiple categories in an interactive and life-long learning fashion to approach the "stability-plasticity dilemma". The problem of incremental learning of multiple categories is still largely unsolved. This is especially true for the domain of cognitive robotics, requiring real-time and interactive learning. To(More)