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19 May 1999 Nonlinear features in vernier acuity
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Proceedings Volume 3644, Human Vision and Electronic Imaging IV; (1999)
Event: Electronic Imaging '99, 1999, San Jose, CA, United States
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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Erhardt Barth, Bettina L. Beard, and Albert J. Ahumada Jr. "Nonlinear features in vernier acuity", Proc. SPIE 3644, Human Vision and Electronic Imaging IV, (19 May 1999);


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