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Proceedings Paper

Nonlinear features in vernier acuity
Author(s): Erhardt Barth; Bettina L. Beard; Albert J. Ahumada
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Paper Abstract

Nonlinear contributions to pattern classification by humans are analyzed by using previously obtained data on discrimination between aligned lines and offset lines. We how that the optimal linear model can be rejected even when the parameters of the model are estimated individually for each observer. We use a new measure of agreement to reject the linear model and to test simple nonlinear operators. The first nonlinearity is position uncertainty. The linear kernels are shrunk to different extents and convolved with the input images. A Gaussian window weights the results of the convolutions and the maximum in that window is selected as the internal variable. The size of the window is chosen such as to maintain a constant total amount of spatial filtering, i.e., the smaller kernels have a larger position uncertainty. The result of two observers indicate that the best agreement is obtained at a moderate degree of position uncertainty, plus-minus one min of arc. Finally, we analyze the effect of orientation uncertainty and show that agreement can be further improved in some cases.

Paper Details

Date Published: 19 May 1999
PDF: 9 pages
Proc. SPIE 3644, Human Vision and Electronic Imaging IV, (19 May 1999); doi: 10.1117/12.348485
Show Author Affiliations
Erhardt Barth, NASA Ames Research Ctr. (Germany)
Bettina L. Beard, NASA Ames Research Ctr. (United States)
Albert J. Ahumada, NASA Ames Research Ctr. (United States)


Published in SPIE Proceedings Vol. 3644:
Human Vision and Electronic Imaging IV
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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