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Analyzing vision at the complexity level

Published online by Cambridge University Press:  19 May 2011

John K. Tsotsos
John K. Tsotsos
Affiliation:
Department of Computer Science, University of Toronto and The Canadian Institute for Advanced Research, 10 King's College Rd., Toronto, Ontario, Canada M5S 1A4. Electronic mail: tsotsos@ai.toronto.edu
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Abstract

The general problem of visual search can be shown to be computationally intractable in a formal, complexity-theoretic sense, yet visual search is extensively involved in everyday perception, and biological systems manage to perform it remarkably well. Complexity level analysis may resolve this contradiction. Visual search can be reshaped into tractability through approximations and by optimizing the resources devoted to visual processing. Architectural constraints can be derived using the minimum cost principle to rule out a large class of potential solutions. The evidence speaks strongly against bottom-up approaches to vision. In particular, the constraints suggest an attentional mechanism that exploits knowledge of the specific problem being solved. This analysis of visual search performance in terms of attentional influences on visual information processing and complexity satisfaction allows a large body of neurophysiological and psychological evidence to be tied together.

Keywords

attentioncomplexitycomputationcortexmatchingsearchvision
Type
Target Article
Information
Behavioral and Brain Sciences , Volume 13 , Issue 3 , September 1990 , pp. 423 - 445
Copyright
Copyright © Cambridge University Press 1990

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