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Open Access

Vision Systems with the Human in the Loop

  • Christian Bauckhage1Email author,
  • Marc Hanheide1,
  • Sebastian Wrede1,
  • Thomas Käster1,
  • Michael Pfeiffer1 and
  • Gerhard Sagerer1
EURASIP Journal on Advances in Signal Processing20052005:302161

Received: 31 December 2003

Published: 25 August 2005


The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed.

Keywords and phrases

cognitive visionadaptionlearningcontextual reasoningarchitectureevaluation

Authors’ Affiliations

Faculty of Technology, Bielefeld University, Bielefeld, Germany


© Christian Bauckhage et al. 2005

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.