Vision Systems with the Human in the Loop
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Vision Systems with the Human in the Loop Christian Bauckhage Faculty of Technology, Bielefeld University, P.O. Box 100131, 33501 Bielefeld, Germany Email: [email protected]
Marc Hanheide Faculty of Technology, Bielefeld University, P.O. Box 100131, 33501 Bielefeld, Germany Email: [email protected]
Sebastian Wrede Faculty of Technology, Bielefeld University, P.O. Box 100131, 33501 Bielefeld, Germany Email: [email protected]
¨ Thomas Kaster Faculty of Technology, Bielefeld University, P.O. Box 100131, 33501 Bielefeld, Germany Email: [email protected]
Michael Pfeiffer Faculty of Technology, Bielefeld University, P.O. Box 100131, 33501 Bielefeld, Germany Email: pfeiff[email protected]
Gerhard Sagerer Faculty of Technology, Bielefeld University, P.O. Box 100131, 33501 Bielefeld, Germany Email: [email protected] Received 31 December 2003; Revised 8 November 2004 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 vision, adaption, learning, contextual reasoning, architecture, evaluation.
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INTRODUCTION
Currently, the computer vision community is witnessing the emergence of a new paradigm. Even though its roots at least date back to work by Crowley and Christensen [1] from the early 1990s, the idea of bringing together the achievements 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.
of 30 years of research in artificial intelligence, automatic perception, machine learning, and robotics was termed cognitive computer vision just recently (cf. [2]). Rather than trying to tackle the philosophical, psychological, or biological subtleties of the question what characterises cognition, we will adopt Christensen’s point of view and restrict ourselves to a limited notion of cognition. Following his argument, we will consider cognition as the generation of k
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