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Proceedings Paper

Technique to extract relevant image features for visual tasks
Author(s): Bettina L. Beard; Albert J. Ahumada
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Paper Abstract

Here we demonstrate a method for constructing stimulus classification images. These images provide information regarding the stimulus aspects the observer uses to segregate images into discrete response categories. Data are first collected on a discrimination task containing low contrast noise. The noises are then averaged separately for the stimulus-response categories. These averages are then summed with appropriate signs to obtain an overall classification image. We determine stimulus classification images for a vernier acuity task to visualize the stimulus features used to make these precise position discriminations. The resulting images reject the idea that the discrimination is performed by the single best discriminating cortical unit. The classification images show one Gabor-like filter for each line, rejecting the nearly ideal assumption of image discrimination models predicting no contribution from the fixed vernier line.

Paper Details

Date Published: 17 July 1998
PDF: 7 pages
Proc. SPIE 3299, Human Vision and Electronic Imaging III, (17 July 1998); doi: 10.1117/12.320099
Show Author Affiliations
Bettina L. Beard, NASA Ames Research Ctr. (United States)
Albert J. Ahumada, NASA Ames Research Ctr. (United States)

Published in SPIE Proceedings Vol. 3299:
Human Vision and Electronic Imaging III
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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