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.
This article is part of the series Advances in Intelligent Vision Systems: Methods and ApplicationsPart II.
Vision Systems with the Human in the Loop
Faculty of Technology, Bielefeld University, P.O. Box 100131, Bielefeld 33501, Germany
EURASIP Journal on Advances in Signal Processing 2005, 2005:302161 doi:10.1155/ASP.2005.2375
The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2005/14/302161
|Received:||31 December 2003|
|Revisions received:||8 November 2004|
|Published:||25 August 2005|
© 2005 Bauckhage et al.
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