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Proceedings Paper

Models of human image discrimination predict object detection in natural backgrounds
Author(s): Albert J. Ahumada; Andrew B. Watson; Ann Marie Rohaly
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Paper Abstract

Object detection involves looking for one of a large set of object subimages in a large set of background images. Image discrimination models predict the probability that an observer will detect a difference between two images. We find that discrimination models can predict the relative detectability of objects in different images, suggesting that these simpler models may be useful in some object detection applications. Six images of a vehicle in an otherwise natural setting were altered to remove the vehicle and mixed with the original image in various proportions. Nineteen observers rated the 24 images for the presence of a vehicle. The pattern of observer detectabilities for the different images was predicted by three discrimination models. A Cortex transform discrimination model, a contrast sensitivity function filter model, and a root-mean-square difference predictor based on the digital image values gave prediction errors of 15%, 49%, and 46%, respectively. Two observers given the same images repeatedly to make the task a discrimination task rated the images similarly, but had detectabilities a factor of two higher.

Paper Details

Date Published: 20 April 1995
PDF: 8 pages
Proc. SPIE 2411, Human Vision, Visual Processing, and Digital Display VI, (20 April 1995); doi: 10.1117/12.207554
Show Author Affiliations
Albert J. Ahumada, NASA Ames Research Ctr. (United States)
Andrew B. Watson, NASA Ames Research Ctr. (United States)
Ann Marie Rohaly, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 2411:
Human Vision, Visual Processing, and Digital Display VI
Bernice E. Rogowitz; Jan P. Allebach, Editor(s)

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