Contour statistics in natural images: grouping across occlusions.

Full 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.

Author information

Author/s: Geisler, Wilson S (WS); Perry, Jeffrey S (JS);

Affiliation: Center for Perceptual Systems and Department of Psychology, University of Texas at Austin, Austin, TX 78712, USA. geisler(-atsign-)psy.utexas.edu

Grants: EY11747 (Agency:NEI NIH HHS) ; R01 EY011747-11 (Agency:NEI NIH HHS)

Journal and publication information

Publication Type: Journal Article; Research Support, N.I.H., Extramural

Journal: Visual neuroscience (Vis Neurosci), published in England. (Language: eng)

Reference: -2009 Jan-Feb; vol 26 (issue 1) : pp 109-21

Dates: Created 2009/03/24; Completed 2009/04/30;

PMID: 19216819, status: MEDLINE (last retrieval date: 4/30/2009, IMS Date: 30 Apr 2009 00:00:00)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Models, Statistical
Pattern Recognition, Visual - physiology
Task Performance and Analysis
Visual Perception - physiology

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