Access the full text.
Sign up today, get DeepDyve free for 14 days.
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
Deriving channel gains from large-area sine-wave contrast sensitivity data MIGUEL A. GARCÍA-PÉREZ1 and VICENTE SIERRA-VÁZQUEZ2 1Departamento de Metodología and 2Departamento de Psicologia Básica-I, Facultad de Psicologia, Universidad Complutense, Campus de Somosaguas, 28223 Madrid, Spain Received 31 August 1993; revised 7 November 1994; accepted 11 November 1994 Abstract-A wealth of detection data can be accounted for by a spatial-vision model including a finite number of space-variant, spatial-frequency and orientation-selective channels of varying gains coupled with a detection rule involving probability summation over space and among channels. This paper shows that the detection of large-area, foveally fixated sine-wave gratings can be understood as if it occurred merely as a result of the activity of the subset of channels whose orientation matches that of the gratings, and operating under a peak-detection rule. This simplification makes it possible to show the theoretical relationship between the large-area sine-wave contrast sensitivity function, the channel gain function, and the channel modulation-transfer functions. It is also shown that the human visual system must have many more channels than are normally assumed in spatial-vision models, for otherwise the contrast sensitivity function would show significant bumps. An unlimited-channel model with a given mathematical form for the channels' modulation
Spatial Vision (continued as Seeing & Perceiving from 2010) – Brill
Published: Jan 1, 1995
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.