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Proceedings Paper

Model of human preattentive visual detection of edge orientation anomalies
Author(s): Virginia H. Brecher; Raymond Bonner; C. Read
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Paper Abstract

Psychophysical studies provide evidence of preattentive visual processing characterized by parallel operations performed on a limited set of features. Since these operations extend well beyond the foveal or high-resolution area of the visual field, one may assume that they are based on lower-resolution features. Such parallelism and data reduction imply computationally efficient processing that could be emulated for machine vision pattern recognition purposes. Several models of preattentive texture segmentation have recently been presented in the computational vision literature. This paper presents a model of human preattentive visual detection of pattern anomalies. Operating on a low-frequency, band-pass filtered image, the model detects singularities by comparing local to global statistics of contrast and edge orientation. The model has been applied to simple schematic images. It successfully predicts the asymmetry in search latencies whereby a target characterized by a preattentively detectable feature 'pops out' of a field of distractors not containing the feature, but when target and distractors are switched, serial search is required to locate the 'odd man out.' The model has also been shown to detect pattern defects on periodic, multilevel integrated circuits.

Paper Details

Date Published: 9 July 1991
PDF: 13 pages
Proc. SPIE 1473, Visual Information Processing: From Neurons to Chips, (9 July 1991); doi: 10.1117/12.45539
Show Author Affiliations
Virginia H. Brecher, IBM/Thomas J. Watson Research Ctr. (United States)
Raymond Bonner, IBM/Thomas J. Watson Research Ctr. (United States)
C. Read, IBM/Thomas J. Watson Research Ctr. (United States)


Published in SPIE Proceedings Vol. 1473:
Visual Information Processing: From Neurons to Chips
Bimal P. Mathur; Christof Koch, Editor(s)

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