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Proceedings Paper

Multiscale retinocortical model of contrast processing
Author(s): Ian R. Moorhead; Nigel D. Haig
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Paper Abstract

Visual performance models have in the past, typically been empirical, relying on the user to supply numerical values such as target contrast and background luminance to describe the performance of the visual system, when undertaking a specified task. However, it is becoming increasingly easy to obtain computer images using for example digital cameras, scanners, imaging photometers and radiometers. We have therefore been examining the possibility of producing a quantitative model of human vision that is capable of directly processing images in order to provide predictions of performance. We are particularly interested in being able to process images of 'real' scenes. The model is inspired by human vision and the components have analogies with parts of the human visual system but their properties are governed primarily by existing psychophysical data. The first stage of the model generates a multiscale, difference of Gaussian (DoG) representation of the image (Burton, Haig and Moorhead), with a central foveal region of high resolution, and with a resolution that declines with eccentricity as the scale of the filter increases. Incorporated into this stage is a gain control process which ensures that the contrast sensitivity is consistent with the psychophysical data of van Nes and Bouman. The second stage incorporates a model of perceived contrast proposed by Cannon and Fullenkamp. Their model assumes the image is analyzed by oriented (Gabor) filters and produces a representation of the image in terms of perceived contrast.

Paper Details

Date Published: 22 April 1996
PDF: 5 pages
Proc. SPIE 2657, Human Vision and Electronic Imaging, (22 April 1996); doi: 10.1117/12.238716
Show Author Affiliations
Ian R. Moorhead, Defence Research Agency Fort Halstead (United Kingdom)
Nigel D. Haig, Defence Research Agency Fort Halstead (United Kingdom)


Published in SPIE Proceedings Vol. 2657:
Human Vision and Electronic Imaging
Bernice E. Rogowitz; Jan P. Allebach, Editor(s)

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