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Early Cognitive Vision: Using Gestalt-Laws for Task-Dependent, Active Image-Processing

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Abstract

The goal of this review is to discuss different strategies employed by the visual system to limit data-flow and to focus data processing. These strategies can be hard-wired, like the eccentricity-dependent visual resolution or they can be dynamically changing like mechanisms of visual attention. We will ask to what degree such strategies are also useful in a computer vision context. Specifically we will discuss, how to adapt them to technical systems where the substrate for the computations is vastly different from that in the brain. It will become clear that most algorithmic principles, which are employed by natural visual systems, need to be reformulated to better fit to modern computer architectures. In addition, we will try to show that it is possible to employ multiple strategies in parallel to arrive at a flexible and robust computer vision system based on recurrent feedback loops and using information derived from the statistics of natural images.

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Authors and Affiliations

  1. Department of Psychology, University of Stirling, Stirling FK9 4LA, U.K

    Florentin Wörgötter, Norbert Krüger & Nicolas Pugeault

  2. Computer Science, Aalborg University, Esbjerg, Denmark

    Norbert Krüger

  3. Institute of Psychology, University Münster, Germany

    Dirk Calow & Markus Lappe

  4. Departement Neurowetenschappen en Psychiatrie, Laboratorium voor Neuro- en Psychofysiologie, KU Leuven, Belgium

    Karl Pauwels & Marc Van Hulle

  5. Department of Psychology, University College London, U.K

    Sovira Tan & Alan Johnston

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  1. Florentin Wörgötter
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  2. Norbert Krüger
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  3. Nicolas Pugeault
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  4. Dirk Calow
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  8. Sovira Tan
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  9. Alan Johnston
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Correspondence to Florentin Wörgötter.

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Wörgötter, F., Krüger, N., Pugeault, N. et al. Early Cognitive Vision: Using Gestalt-Laws for Task-Dependent, Active Image-Processing. Natural Computing 3, 293–321 (2004). https://doi.org/10.1023/B:NACO.0000036817.38320.fe

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