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Impossible objects and the things we do first in vision.

R I Cowie1

  • 1Department of Psychology, Queen's University Belfast, Northern Ireland.

British Journal of Psychology (London, England : 1953)
|August 1, 1988
PubMed
Summary
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Intermediate-level image interpretation uses specific postulates, leading to occasional acceptance of inconsistent interpretations, similar to human perception of impossible objects. This suggests vision prioritizes edge orientation over depth for computational efficiency.

Area of Science:

  • Computer Vision
  • Cognitive Psychology
  • Image Analysis

Background:

  • Current image interpretation methods often focus on large or small classes of structures.
  • Intermediate-sized classes of structures in scene analysis present unique challenges and properties.
  • Incomplete analysis of underlying postulates can lead to inconsistent interpretations.

Purpose of the Study:

  • To explore the properties of image analysis programs using intermediate-sized classes.
  • To investigate the connection between impossible objects and the postulates used in image interpretation.
  • To determine the specific postulates and their development level in human vision.

Main Methods:

  • Analysis of scene analysis programs with intermediate-sized classes.

Related Experiment Videos

  • Examination of the phenomenon of 'Impossible Objects' in human perception.
  • Experimental demonstrations and data analysis to identify visual postulates.

    • Main Results:

    • Inconsistent interpretations are occasionally accepted due to incomplete postulate analysis.
    • Impossible objects suggest human vision uses intermediate postulates, deriving only a subset of implications.
    • Postulates primarily concern angles between edges, with automatic derivation and checking of orientation implications, not depth.

    Conclusions:

    • Human vision, like intermediate-class image analysis, may prioritize edge orientation over depth for ecological and computational reasons.
    • The study of impossible objects can reveal the specific postulates and their development in visual processing.
    • Understanding these postulates offers insights into efficient information extraction in computer vision and human perception.