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Proceedings Paper

Evaluation of human vision models for predicting human observer performance
Author(s): Warren B. Jackson; Maya R. Said; David A. Jared; James O. Larimer; Jennifer Gille; Jeffrey Lubin
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Paper Abstract

We demonstrate that human-vision-model-based image quality metrics not only correlate strongly with subjective evaluations of image quality but also with human observer performance on visual recognition tasks. By varying amorphous silicon image system design parameters, the performance of human observers in target identification using the resulting test images was measured, and compared with the target weighted just-noticeable-difference produced by a human vision model applied to the same set of images. The detectability of model observer with the human observer was highly correlated for a wide range of image system design parameters. These results demonstrate that the human vision model can be used to produce human observer performance optimized imaging systems without the need for extensive human trials. The human vision based tumor detectors represent a generalization of channelized Hotelling models to non-linear, perceptually based models.

Paper Details

Date Published: 16 April 1997
PDF: 10 pages
Proc. SPIE 3036, Medical Imaging 1997: Image Perception, (16 April 1997); doi: 10.1117/12.271312
Show Author Affiliations
Warren B. Jackson, Xerox Palo Alto Research Ctr. (United States)
Maya R. Said, Xerox Palo Alto Research Ctr. (United States)
David A. Jared, Xerox Palo Alto Research Ctr. (United States)
James O. Larimer, NASA Ames Research Ctr. (United States)
Jennifer Gille, Western Aerospace Labs. (United States)
Jeffrey Lubin, David Sarnoff Research Ctr. (United States)

Published in SPIE Proceedings Vol. 3036:
Medical Imaging 1997: Image Perception
Harold L. Kundel, Editor(s)

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