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Knowledge-based vision and simple visual machines

D Cliff1, J Noble

  • 1School of Cognitive and Computing Sciences, University of Sussex, Brighton, UK. davec@cogs.susx.ac.uk

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|August 29, 1997
PubMed
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Machine vision systems evolved artificially challenge the concept of internal representations. This study questions whether posited representations in natural vision systems are real or merely placeholders for unknown mechanisms.

Area of Science:

  • Artificial Intelligence
  • Machine Vision
  • Computational Neuroscience
  • Evolutionary Computation

Background:

  • Traditional machine vision relies on internal representations (knowledge-based or model-based vision).
  • The existence and nature of these representations are often assumed rather than operationally defined.
  • This assumption is prevalent in understanding both artificial and natural (animal) vision systems.

Purpose of the Study:

  • To investigate machine vision systems developed through artificial evolution.
  • To critically examine the concept of internal representations in evolved systems.
  • To question the validity of posited representations in natural vision.

Main Methods:

  • Analysis of simple machine vision systems created via artificial evolution.

Related Experiment Videos

  • Examination of the challenges in defining representations at a causal mechanistic level.
  • Theoretical critique of knowledge-based vision approaches applied to evolved systems.

    • Main Results:

    • Identifying internal representations in artificially evolved systems is difficult due to a lack of operational definitions.
    • The existence of representations in natural vision systems is questioned.
    • Representations inferred by external observers may be illusory or placeholders.

    Conclusions:

    • The knowledge-based vision approach may lead to incorrect models of evolved systems (artificial or natural).
    • Causal mechanistic interactions are key, and representations may obscure these.
    • Rethinking the role and definition of representations in vision science is necessary.