A Biologically Motivated System for Unconstrained Online Learning of Visual Objects

  • Heiko Wersing
  • Stephan Kirstein
  • Michael Götting
  • Holger Brandl
  • Mark Dunn
  • Inna Mikhailova
  • Christian Goerick
  • Jochen Steil
  • Helge Ritter
  • Edgar Körner
Conference paper

DOI: 10.1007/11840930_53

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)
Cite this paper as:
Wersing H. et al. (2006) A Biologically Motivated System for Unconstrained Online Learning of Visual Objects. In: Kollias S., Stafylopatis A., Duch W., Oja E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg

Abstract

We present a biologically motivated system for object recognition that is capable of online learning of several objects based on interaction with a human teacher. The training is unconstrained in the sense that arbitrary objects can be freely presented in front of a stereo camera system and labeled by speech input. The architecture unites biological principles such as appearance-based representation in topographical feature detection hierarchies and context-driven transfer between different levels of object memory. The learning is fully online and thus avoids an artificial separation of the interaction into training and test phases.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Heiko Wersing
    • 1
  • Stephan Kirstein
    • 1
  • Michael Götting
    • 2
  • Holger Brandl
    • 1
  • Mark Dunn
    • 1
  • Inna Mikhailova
    • 1
  • Christian Goerick
    • 1
  • Jochen Steil
    • 2
  • Helge Ritter
    • 2
  • Edgar Körner
    • 1
  1. 1.Honda Research Institute Europe GmbHOffenbach/MainGermany
  2. 2.Neuroinformatics Group, Faculty of TechnologyBielefeld UniversityBielefeldGermany

Personalised recommendations