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Contour statistics in natural images: Grouping across occlusions

Published online by Cambridge University Press:  01 January 2009

WILSON S. GEISLER  and
JEFFREY S. PERRY
WILSON S. GEISLER*
Affiliation:
Center for Perceptual Systems and Department of Psychology, University of Texas at Austin, Austin TX
JEFFREY S. PERRY
Affiliation:
Center for Perceptual Systems and Department of Psychology, University of Texas at Austin, Austin TX
*
*Address correspondence and reprint requests to: Wilson S. Geisler, Center for Perceptual Systems, 1 University Station A8000, University of Texas at Austin, Austin, TX 78712. E-mail: geisler@psy.utexas.edu
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Abstract

Correctly interpreting a natural image requires dealing properly with the effects of occlusion, and hence, contour grouping across occlusions is a major component of many natural visual tasks. To better understand the mechanisms of contour grouping across occlusions, we (a) measured the pair-wise statistics of edge elements from contours in natural images, as a function of edge element geometry and contrast polarity, (b) derived the ideal Bayesian observer for a contour occlusion task where the stimuli were extracted directly from natural images, and then (c) measured human performance in the same contour occlusion task. In addition to discovering new statistical properties of natural contours, we found that naïve human observers closely parallel ideal performance in our contour occlusion task. In fact, there was no region of the four-dimensional stimulus space (three geometry dimensions and one contrast dimension) where humans did not closely parallel the performance of the ideal observer (i.e., efficiency was approximately constant over the entire space). These results reject many other contour grouping hypotheses and strongly suggest that the neural mechanisms of contour grouping are tightly related to the statistical properties of contours in natural images.

Information

Type
Natural Scene Statistics and Natural Tasks
Information
Visual Neuroscience , Volume 26 , Issue 1 , January 2009 , pp. 109 - 121
Copyright
Copyright © Cambridge University Press 2009

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